Overview

Dataset statistics

Number of variables28
Number of observations115
Missing cells62
Missing cells (%)1.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory25.3 KiB
Average record size in memory225.1 B

Variable types

Numeric9
Categorical19

Alerts

airdate has constant value "2020-12-24" Constant
url has a high cardinality: 115 distinct values High cardinality
name has a high cardinality: 85 distinct values High cardinality
_embedded_show_url has a high cardinality: 72 distinct values High cardinality
_embedded_show_name has a high cardinality: 72 distinct values High cardinality
_embedded_show_premiered has a high cardinality: 54 distinct values High cardinality
_embedded_show_officialSite has a high cardinality: 63 distinct values High cardinality
_embedded_show_summary has a high cardinality: 65 distinct values High cardinality
_links_self_href has a high cardinality: 115 distinct values High cardinality
season is highly correlated with _embedded_show_updatedHigh correlation
runtime is highly correlated with _embedded_show_runtime and 1 other fieldsHigh correlation
_embedded_show_runtime is highly correlated with runtime and 1 other fieldsHigh correlation
_embedded_show_averageRuntime is highly correlated with runtime and 1 other fieldsHigh correlation
_embedded_show_weight is highly correlated with _embedded_show_updatedHigh correlation
_embedded_show_updated is highly correlated with season and 1 other fieldsHigh correlation
season is highly correlated with numberHigh correlation
number is highly correlated with seasonHigh correlation
runtime is highly correlated with _embedded_show_runtime and 1 other fieldsHigh correlation
_embedded_show_runtime is highly correlated with runtime and 1 other fieldsHigh correlation
_embedded_show_averageRuntime is highly correlated with runtime and 1 other fieldsHigh correlation
_embedded_show_weight is highly correlated with _embedded_show_updatedHigh correlation
_embedded_show_updated is highly correlated with _embedded_show_weightHigh correlation
runtime is highly correlated with _embedded_show_runtime and 1 other fieldsHigh correlation
_embedded_show_runtime is highly correlated with runtime and 1 other fieldsHigh correlation
_embedded_show_averageRuntime is highly correlated with runtime and 1 other fieldsHigh correlation
name is highly correlated with _embedded_show_officialSite and 4 other fieldsHigh correlation
_embedded_show_officialSite is highly correlated with name and 13 other fieldsHigh correlation
_embedded_show_ended is highly correlated with _embedded_show_officialSite and 5 other fieldsHigh correlation
_embedded_show_name is highly correlated with _embedded_show_officialSite and 13 other fieldsHigh correlation
summary is highly correlated with airdateHigh correlation
_embedded_show_summary is highly correlated with _embedded_show_officialSite and 12 other fieldsHigh correlation
_embedded_show_genres is highly correlated with _embedded_show_officialSite and 7 other fieldsHigh correlation
type is highly correlated with name and 5 other fieldsHigh correlation
_embedded_show_type is highly correlated with _embedded_show_officialSite and 6 other fieldsHigh correlation
airdate is highly correlated with name and 15 other fieldsHigh correlation
airtime is highly correlated with _embedded_show_name and 5 other fieldsHigh correlation
_embedded_show_status is highly correlated with _embedded_show_officialSite and 7 other fieldsHigh correlation
_embedded_show_premiered is highly correlated with name and 12 other fieldsHigh correlation
_embedded_show_url is highly correlated with _embedded_show_officialSite and 13 other fieldsHigh correlation
_embedded_show_language is highly correlated with _embedded_show_officialSite and 4 other fieldsHigh correlation
_embedded_show_dvdCountry is highly correlated with name and 7 other fieldsHigh correlation
airstamp is highly correlated with _embedded_show_officialSite and 7 other fieldsHigh correlation
id is highly correlated with name and 15 other fieldsHigh correlation
name is highly correlated with id and 20 other fieldsHigh correlation
season is highly correlated with id and 6 other fieldsHigh correlation
number is highly correlated with name and 10 other fieldsHigh correlation
type is highly correlated with name and 7 other fieldsHigh correlation
airtime is highly correlated with airstamp and 12 other fieldsHigh correlation
airstamp is highly correlated with name and 18 other fieldsHigh correlation
runtime is highly correlated with name and 13 other fieldsHigh correlation
summary is highly correlated with name and 3 other fieldsHigh correlation
_embedded_show_id is highly correlated with id and 18 other fieldsHigh correlation
_embedded_show_url is highly correlated with id and 22 other fieldsHigh correlation
_embedded_show_name is highly correlated with id and 22 other fieldsHigh correlation
_embedded_show_type is highly correlated with id and 16 other fieldsHigh correlation
_embedded_show_language is highly correlated with id and 16 other fieldsHigh correlation
_embedded_show_genres is highly correlated with id and 19 other fieldsHigh correlation
_embedded_show_status is highly correlated with name and 15 other fieldsHigh correlation
_embedded_show_runtime is highly correlated with id and 17 other fieldsHigh correlation
_embedded_show_averageRuntime is highly correlated with name and 16 other fieldsHigh correlation
_embedded_show_premiered is highly correlated with id and 22 other fieldsHigh correlation
_embedded_show_ended is highly correlated with id and 14 other fieldsHigh correlation
_embedded_show_officialSite is highly correlated with id and 21 other fieldsHigh correlation
_embedded_show_weight is highly correlated with id and 19 other fieldsHigh correlation
_embedded_show_dvdCountry is highly correlated with id and 7 other fieldsHigh correlation
_embedded_show_summary is highly correlated with id and 21 other fieldsHigh correlation
_embedded_show_updated is highly correlated with id and 14 other fieldsHigh correlation
number has 2 (1.7%) missing values Missing
runtime has 12 (10.4%) missing values Missing
_embedded_show_runtime has 38 (33.0%) missing values Missing
_embedded_show_averageRuntime has 10 (8.7%) missing values Missing
url is uniformly distributed Uniform
_links_self_href is uniformly distributed Uniform
id has unique values Unique
url has unique values Unique
_links_self_href has unique values Unique

Reproduction

Analysis started2022-05-10 02:19:56.859007
Analysis finished2022-05-10 02:20:32.193599
Duration35.33 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

id
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct115
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2037701.643
Minimum1949912
Maximum2324415
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2022-05-09T21:20:32.261384image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1949912
5-th percentile1962244.3
Q11985633
median1992696
Q32032665
95-th percentile2314594.6
Maximum2324415
Range374503
Interquartile range (IQR)47032

Descriptive statistics

Standard deviation101773.9523
Coefficient of variation (CV)0.04994546314
Kurtosis2.300010772
Mean2037701.643
Median Absolute Deviation (MAD)13909
Skewness1.88264804
Sum234335689
Variance1.035793737 × 1010
MonotonicityNot monotonic
2022-05-09T21:20:32.381068image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19779001
 
0.9%
20053231
 
0.9%
20000661
 
0.9%
19975271
 
0.9%
19975261
 
0.9%
19884041
 
0.9%
19854781
 
0.9%
19854771
 
0.9%
20054191
 
0.9%
19787871
 
0.9%
Other values (105)105
91.3%
ValueCountFrequency (%)
19499121
0.9%
19499131
0.9%
19503681
0.9%
19507021
0.9%
19553171
0.9%
19607331
0.9%
19628921
0.9%
19639991
0.9%
19643941
0.9%
19725731
0.9%
ValueCountFrequency (%)
23244151
0.9%
23244141
0.9%
23244131
0.9%
23244121
0.9%
23244111
0.9%
23244101
0.9%
23103881
0.9%
22893791
0.9%
22893251
0.9%
22616471
0.9%

url
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct115
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
https://www.tvmaze.com/episodes/1977900/obycnaa-zensina-2x04-seria-13
 
1
https://www.tvmaze.com/episodes/2005323/laikykites-ten-5x16-kalediniai-burtai-ir-ypatingos-eglutes
 
1
https://www.tvmaze.com/episodes/2000066/ultimate-note-1x19-episode-19
 
1
https://www.tvmaze.com/episodes/1997527/the-penalty-zone-1x20-episode-20
 
1
https://www.tvmaze.com/episodes/1997526/the-penalty-zone-1x19-episode-19
 
1
Other values (110)
110 

Length

Max length145
Median length105
Mean length80.93043478
Min length58

Characters and Unicode

Total characters9307
Distinct characters40
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique115 ?
Unique (%)100.0%

Sample

1st rowhttps://www.tvmaze.com/episodes/1977900/obycnaa-zensina-2x04-seria-13
2nd rowhttps://www.tvmaze.com/episodes/1963999/257-pricin-ctoby-zit-2x09-seria-22
3rd rowhttps://www.tvmaze.com/episodes/1949912/smesariki-novyj-sezon-1x33-zagvozdka
4th rowhttps://www.tvmaze.com/episodes/1949913/smesariki-novyj-sezon-1x34-starinnyj-novogodnij-obycaj
5th rowhttps://www.tvmaze.com/episodes/1960733/psih-1x08-vozrozdenie

Common Values

ValueCountFrequency (%)
https://www.tvmaze.com/episodes/1977900/obycnaa-zensina-2x04-seria-131
 
0.9%
https://www.tvmaze.com/episodes/2005323/laikykites-ten-5x16-kalediniai-burtai-ir-ypatingos-eglutes1
 
0.9%
https://www.tvmaze.com/episodes/2000066/ultimate-note-1x19-episode-191
 
0.9%
https://www.tvmaze.com/episodes/1997527/the-penalty-zone-1x20-episode-201
 
0.9%
https://www.tvmaze.com/episodes/1997526/the-penalty-zone-1x19-episode-191
 
0.9%
https://www.tvmaze.com/episodes/1988404/love-teenager-1x02-school-life-after-separation-with-first-class-boyfriend1
 
0.9%
https://www.tvmaze.com/episodes/1985478/you-complete-me-1x22-episode-221
 
0.9%
https://www.tvmaze.com/episodes/1985477/you-complete-me-1x21-episode-211
 
0.9%
https://www.tvmaze.com/episodes/2005419/yes-chef-1x02-omsvarmad-av-bin-pa-gotland1
 
0.9%
https://www.tvmaze.com/episodes/1978787/offgun-mommy-taste-1x10-episode-101
 
0.9%
Other values (105)105
91.3%

Length

2022-05-09T21:20:32.512843image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://www.tvmaze.com/episodes/1977900/obycnaa-zensina-2x04-seria-131
 
0.9%
https://www.tvmaze.com/episodes/1999303/futmallscom-1x08-episode-81
 
0.9%
https://www.tvmaze.com/episodes/1949912/smesariki-novyj-sezon-1x33-zagvozdka1
 
0.9%
https://www.tvmaze.com/episodes/1949913/smesariki-novyj-sezon-1x34-starinnyj-novogodnij-obycaj1
 
0.9%
https://www.tvmaze.com/episodes/1960733/psih-1x08-vozrozdenie1
 
0.9%
https://www.tvmaze.com/episodes/1982409/volk-1x11-seria-111
 
0.9%
https://www.tvmaze.com/episodes/1982410/volk-1x12-seria-121
 
0.9%
https://www.tvmaze.com/episodes/1987502/passaziry-1x01-svetlana-i-igor1
 
0.9%
https://www.tvmaze.com/episodes/1987720/passaziry-1x02-saska1
 
0.9%
https://www.tvmaze.com/episodes/1985788/theres-a-pit-in-my-senior-martial-brothers-brain-2x10-episode-101
 
0.9%
Other values (105)105
91.3%

Most occurring characters

ValueCountFrequency (%)
e773
 
8.3%
-711
 
7.6%
s608
 
6.5%
/575
 
6.2%
t560
 
6.0%
o520
 
5.6%
i406
 
4.4%
w377
 
4.1%
p361
 
3.9%
a360
 
3.9%
Other values (30)4056
43.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter6365
68.4%
Decimal Number1311
 
14.1%
Other Punctuation920
 
9.9%
Dash Punctuation711
 
7.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e773
12.1%
s608
 
9.6%
t560
 
8.8%
o520
 
8.2%
i406
 
6.4%
w377
 
5.9%
p361
 
5.7%
a360
 
5.7%
m331
 
5.2%
d279
 
4.4%
Other values (16)1790
28.1%
Decimal Number
ValueCountFrequency (%)
1262
20.0%
2212
16.2%
0185
14.1%
9176
13.4%
393
 
7.1%
493
 
7.1%
582
 
6.3%
878
 
5.9%
777
 
5.9%
653
 
4.0%
Other Punctuation
ValueCountFrequency (%)
/575
62.5%
.230
 
25.0%
:115
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
-711
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin6365
68.4%
Common2942
31.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e773
12.1%
s608
 
9.6%
t560
 
8.8%
o520
 
8.2%
i406
 
6.4%
w377
 
5.9%
p361
 
5.7%
a360
 
5.7%
m331
 
5.2%
d279
 
4.4%
Other values (16)1790
28.1%
Common
ValueCountFrequency (%)
-711
24.2%
/575
19.5%
1262
 
8.9%
.230
 
7.8%
2212
 
7.2%
0185
 
6.3%
9176
 
6.0%
:115
 
3.9%
393
 
3.2%
493
 
3.2%
Other values (4)290
9.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII9307
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e773
 
8.3%
-711
 
7.6%
s608
 
6.5%
/575
 
6.2%
t560
 
6.0%
o520
 
5.6%
i406
 
4.4%
w377
 
4.1%
p361
 
3.9%
a360
 
3.9%
Other values (30)4056
43.6%

name
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct85
Distinct (%)73.9%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
Episode 5
 
6
Episode 3
 
5
Episode 4
 
4
Episode 6
 
4
Episode 2
 
4
Other values (80)
92 

Length

Max length98
Median length74
Mean length19.6
Min length5

Characters and Unicode

Total characters2254
Distinct characters140
Distinct categories13 ?
Distinct scripts5 ?
Distinct blocks7 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique69 ?
Unique (%)60.0%

Sample

1st rowСерия 13
2nd rowСерия 22
3rd rowЗагвоздка
4th rowСтаринный новогодний обычай
5th rowВозрождение

Common Values

ValueCountFrequency (%)
Episode 56
 
5.2%
Episode 35
 
4.3%
Episode 44
 
3.5%
Episode 64
 
3.5%
Episode 24
 
3.5%
Episode 13
 
2.6%
Episode 82
 
1.7%
Episode 192
 
1.7%
Episode 202
 
1.7%
Episode 222
 
1.7%
Other values (75)81
70.4%

Length

2022-05-09T21:20:32.622933image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
episode49
 
12.1%
the16
 
3.9%
11
 
2.7%
59
 
2.2%
37
 
1.7%
17
 
1.7%
46
 
1.5%
26
 
1.5%
confetti6
 
1.5%
of6
 
1.5%
Other values (228)283
69.7%

Most occurring characters

ValueCountFrequency (%)
291
 
12.9%
e156
 
6.9%
i132
 
5.9%
o106
 
4.7%
s101
 
4.5%
t87
 
3.9%
d76
 
3.4%
p74
 
3.3%
a69
 
3.1%
n56
 
2.5%
Other values (130)1106
49.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1505
66.8%
Space Separator291
 
12.9%
Uppercase Letter271
 
12.0%
Decimal Number113
 
5.0%
Other Punctuation47
 
2.1%
Other Letter14
 
0.6%
Dash Punctuation6
 
0.3%
Math Symbol2
 
0.1%
Initial Punctuation1
 
< 0.1%
Final Punctuation1
 
< 0.1%
Other values (3)3
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e156
 
10.4%
i132
 
8.8%
o106
 
7.0%
s101
 
6.7%
t87
 
5.8%
d76
 
5.0%
p74
 
4.9%
a69
 
4.6%
n56
 
3.7%
r56
 
3.7%
Other values (51)592
39.3%
Uppercase Letter
ValueCountFrequency (%)
E54
19.9%
T19
 
7.0%
S18
 
6.6%
C17
 
6.3%
B16
 
5.9%
P10
 
3.7%
W9
 
3.3%
O9
 
3.3%
К9
 
3.3%
H8
 
3.0%
Other values (31)102
37.6%
Decimal Number
ValueCountFrequency (%)
230
26.5%
122
19.5%
317
15.0%
011
 
9.7%
510
 
8.8%
49
 
8.0%
66
 
5.3%
83
 
2.7%
73
 
2.7%
92
 
1.8%
Other Letter
ValueCountFrequency (%)
ن3
21.4%
و2
14.3%
ا2
14.3%
ك1
 
7.1%
ر1
 
7.1%
ش1
 
7.1%
د1
 
7.1%
ع1
 
7.1%
م1
 
7.1%
1
 
7.1%
Other Punctuation
ValueCountFrequency (%)
'14
29.8%
:10
21.3%
,9
19.1%
/5
 
10.6%
.4
 
8.5%
?2
 
4.3%
*1
 
2.1%
&1
 
2.1%
#1
 
2.1%
Dash Punctuation
ValueCountFrequency (%)
-4
66.7%
2
33.3%
Space Separator
ValueCountFrequency (%)
291
100.0%
Math Symbol
ValueCountFrequency (%)
|2
100.0%
Initial Punctuation
ValueCountFrequency (%)
«1
100.0%
Final Punctuation
ValueCountFrequency (%)
»1
100.0%
Other Symbol
ValueCountFrequency (%)
1
100.0%
Open Punctuation
ValueCountFrequency (%)
(1
100.0%
Close Punctuation
ValueCountFrequency (%)
)1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1373
60.9%
Common464
 
20.6%
Cyrillic403
 
17.9%
Arabic13
 
0.6%
Han1
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e156
 
11.4%
i132
 
9.6%
o106
 
7.7%
s101
 
7.4%
t87
 
6.3%
d76
 
5.5%
p74
 
5.4%
a69
 
5.0%
n56
 
4.1%
r56
 
4.1%
Other values (43)460
33.5%
Cyrillic
ValueCountFrequency (%)
о48
 
11.9%
а37
 
9.2%
р33
 
8.2%
и24
 
6.0%
е24
 
6.0%
н20
 
5.0%
с18
 
4.5%
к17
 
4.2%
в16
 
4.0%
л12
 
3.0%
Other values (39)154
38.2%
Common
ValueCountFrequency (%)
291
62.7%
230
 
6.5%
122
 
4.7%
317
 
3.7%
'14
 
3.0%
011
 
2.4%
:10
 
2.2%
510
 
2.2%
49
 
1.9%
,9
 
1.9%
Other values (18)41
 
8.8%
Arabic
ValueCountFrequency (%)
ن3
23.1%
و2
15.4%
ا2
15.4%
ك1
 
7.7%
ر1
 
7.7%
ش1
 
7.7%
د1
 
7.7%
ع1
 
7.7%
م1
 
7.7%
Han
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1820
80.7%
Cyrillic403
 
17.9%
None14
 
0.6%
Arabic13
 
0.6%
Punctuation2
 
0.1%
CJK1
 
< 0.1%
Letterlike Symbols1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
291
16.0%
e156
 
8.6%
i132
 
7.3%
o106
 
5.8%
s101
 
5.5%
t87
 
4.8%
d76
 
4.2%
p74
 
4.1%
a69
 
3.8%
n56
 
3.1%
Other values (61)672
36.9%
Cyrillic
ValueCountFrequency (%)
о48
 
11.9%
а37
 
9.2%
р33
 
8.2%
и24
 
6.0%
е24
 
6.0%
н20
 
5.0%
с18
 
4.5%
к17
 
4.2%
в16
 
4.0%
л12
 
3.0%
Other values (39)154
38.2%
None
ValueCountFrequency (%)
ö3
21.4%
ü3
21.4%
ė2
14.3%
å2
14.3%
«1
 
7.1%
»1
 
7.1%
ä1
 
7.1%
ã1
 
7.1%
Arabic
ValueCountFrequency (%)
ن3
23.1%
و2
15.4%
ا2
15.4%
ك1
 
7.7%
ر1
 
7.7%
ش1
 
7.7%
د1
 
7.7%
ع1
 
7.7%
م1
 
7.7%
Punctuation
ValueCountFrequency (%)
2
100.0%
CJK
ValueCountFrequency (%)
1
100.0%
Letterlike Symbols
ValueCountFrequency (%)
1
100.0%

season
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct13
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean108.0086957
Minimum1
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2022-05-09T21:20:32.717317image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32.5
95-th percentile641.7
Maximum2020
Range2019
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation450.5891172
Coefficient of variation (CV)4.171785563
Kurtosis14.90812086
Mean108.0086957
Median Absolute Deviation (MAD)0
Skewness4.079927175
Sum12421
Variance203030.5526
MonotonicityNot monotonic
2022-05-09T21:20:32.799910image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
168
59.1%
218
 
15.7%
38
 
7.0%
20206
 
5.2%
45
 
4.3%
53
 
2.6%
81
 
0.9%
61
 
0.9%
181
 
0.9%
151
 
0.9%
Other values (3)3
 
2.6%
ValueCountFrequency (%)
168
59.1%
218
 
15.7%
38
 
7.0%
45
 
4.3%
53
 
2.6%
61
 
0.9%
81
 
0.9%
91
 
0.9%
151
 
0.9%
181
 
0.9%
ValueCountFrequency (%)
20206
5.2%
511
 
0.9%
311
 
0.9%
181
 
0.9%
151
 
0.9%
91
 
0.9%
81
 
0.9%
61
 
0.9%
53
2.6%
45
4.3%

number
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct38
Distinct (%)33.6%
Missing2
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean16.95575221
Minimum1
Maximum351
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2022-05-09T21:20:33.039656image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q320
95-th percentile52.8
Maximum351
Range350
Interquartile range (IQR)16

Descriptive statistics

Standard deviation35.6982366
Coefficient of variation (CV)2.105376167
Kurtosis69.42972939
Mean16.95575221
Median Absolute Deviation (MAD)5
Skewness7.599904357
Sum1916
Variance1274.364096
MonotonicityNot monotonic
2022-05-09T21:20:33.149985image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
211
 
9.6%
59
 
7.8%
39
 
7.8%
48
 
7.0%
18
 
7.0%
77
 
6.1%
87
 
6.1%
65
 
4.3%
104
 
3.5%
114
 
3.5%
Other values (28)41
35.7%
ValueCountFrequency (%)
18
7.0%
211
9.6%
39
7.8%
48
7.0%
59
7.8%
65
4.3%
77
6.1%
87
6.1%
92
 
1.7%
104
 
3.5%
ValueCountFrequency (%)
3511
0.9%
851
0.9%
731
0.9%
681
0.9%
571
0.9%
541
0.9%
522
1.7%
491
0.9%
431
0.9%
391
0.9%

type
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
regular
113 
insignificant_special
 
1
significant_special
 
1

Length

Max length21
Median length7
Mean length7.226086957
Min length7

Characters and Unicode

Total characters831
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.7%

Sample

1st rowregular
2nd rowregular
3rd rowregular
4th rowregular
5th rowregular

Common Values

ValueCountFrequency (%)
regular113
98.3%
insignificant_special1
 
0.9%
significant_special1
 
0.9%

Length

2022-05-09T21:20:33.260908image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T21:20:33.348933image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
regular113
98.3%
insignificant_special1
 
0.9%
significant_special1
 
0.9%

Most occurring characters

ValueCountFrequency (%)
r226
27.2%
a117
14.1%
e115
13.8%
g115
13.8%
l115
13.8%
u113
13.6%
i9
 
1.1%
n5
 
0.6%
s4
 
0.5%
c4
 
0.5%
Other values (4)8
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter829
99.8%
Connector Punctuation2
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r226
27.3%
a117
14.1%
e115
13.9%
g115
13.9%
l115
13.9%
u113
13.6%
i9
 
1.1%
n5
 
0.6%
s4
 
0.5%
c4
 
0.5%
Other values (3)6
 
0.7%
Connector Punctuation
ValueCountFrequency (%)
_2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin829
99.8%
Common2
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
r226
27.3%
a117
14.1%
e115
13.9%
g115
13.9%
l115
13.9%
u113
13.6%
i9
 
1.1%
n5
 
0.6%
s4
 
0.5%
c4
 
0.5%
Other values (3)6
 
0.7%
Common
ValueCountFrequency (%)
_2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII831
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r226
27.2%
a117
14.1%
e115
13.8%
g115
13.8%
l115
13.8%
u113
13.6%
i9
 
1.1%
n5
 
0.6%
s4
 
0.5%
c4
 
0.5%
Other values (4)8
 
1.0%

airdate
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
2020-12-24
115 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1150
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-12-24
2nd row2020-12-24
3rd row2020-12-24
4th row2020-12-24
5th row2020-12-24

Common Values

ValueCountFrequency (%)
2020-12-24115
100.0%

Length

2022-05-09T21:20:33.433822image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T21:20:33.513009image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-12-24115
100.0%

Most occurring characters

ValueCountFrequency (%)
2460
40.0%
0230
20.0%
-230
20.0%
1115
 
10.0%
4115
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number920
80.0%
Dash Punctuation230
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2460
50.0%
0230
25.0%
1115
 
12.5%
4115
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
-230
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1150
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2460
40.0%
0230
20.0%
-230
20.0%
1115
 
10.0%
4115
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1150
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2460
40.0%
0230
20.0%
-230
20.0%
1115
 
10.0%
4115
 
10.0%

airtime
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct14
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
nan
86 
12:00
10 
20:00
 
4
06:00
 
3
10:00
 
2
Other values (9)
10 

Length

Max length5
Median length3
Mean length3.504347826
Min length3

Characters and Unicode

Total characters403
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)7.0%

Sample

1st row10:00
2nd rownan
3rd rownan
4th rownan
5th row12:00

Common Values

ValueCountFrequency (%)
nan86
74.8%
12:0010
 
8.7%
20:004
 
3.5%
06:003
 
2.6%
10:002
 
1.7%
21:002
 
1.7%
11:001
 
0.9%
17:001
 
0.9%
20:201
 
0.9%
18:001
 
0.9%
Other values (4)4
 
3.5%

Length

2022-05-09T21:20:33.591122image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nan86
74.8%
12:0010
 
8.7%
20:004
 
3.5%
06:003
 
2.6%
10:002
 
1.7%
21:002
 
1.7%
11:001
 
0.9%
17:001
 
0.9%
20:201
 
0.9%
18:001
 
0.9%
Other values (4)4
 
3.5%

Most occurring characters

ValueCountFrequency (%)
n172
42.7%
a86
21.3%
067
 
16.6%
:29
 
7.2%
120
 
5.0%
219
 
4.7%
63
 
0.7%
92
 
0.5%
52
 
0.5%
71
 
0.2%
Other values (2)2
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter258
64.0%
Decimal Number116
28.8%
Other Punctuation29
 
7.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
067
57.8%
120
 
17.2%
219
 
16.4%
63
 
2.6%
92
 
1.7%
52
 
1.7%
71
 
0.9%
81
 
0.9%
41
 
0.9%
Lowercase Letter
ValueCountFrequency (%)
n172
66.7%
a86
33.3%
Other Punctuation
ValueCountFrequency (%)
:29
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin258
64.0%
Common145
36.0%

Most frequent character per script

Common
ValueCountFrequency (%)
067
46.2%
:29
20.0%
120
 
13.8%
219
 
13.1%
63
 
2.1%
92
 
1.4%
52
 
1.4%
71
 
0.7%
81
 
0.7%
41
 
0.7%
Latin
ValueCountFrequency (%)
n172
66.7%
a86
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII403
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n172
42.7%
a86
21.3%
067
 
16.6%
:29
 
7.2%
120
 
5.0%
219
 
4.7%
63
 
0.7%
92
 
0.5%
52
 
0.5%
71
 
0.2%
Other values (2)2
 
0.5%

airstamp
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct19
Distinct (%)16.5%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
2020-12-24T12:00:00+00:00
33 
2020-12-24T04:00:00+00:00
24 
2020-12-24T06:30:00+00:00
14 
2020-12-24T17:00:00+00:00
2020-12-24T00:00:00+00:00
Other values (14)
27 

Length

Max length25
Median length25
Mean length25
Min length25

Characters and Unicode

Total characters2875
Distinct characters14
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)5.2%

Sample

1st row2020-12-23T22:00:00+00:00
2nd row2020-12-24T00:00:00+00:00
3rd row2020-12-24T00:00:00+00:00
4th row2020-12-24T00:00:00+00:00
5th row2020-12-24T00:00:00+00:00

Common Values

ValueCountFrequency (%)
2020-12-24T12:00:00+00:0033
28.7%
2020-12-24T04:00:00+00:0024
20.9%
2020-12-24T06:30:00+00:0014
12.2%
2020-12-24T17:00:00+00:009
 
7.8%
2020-12-24T00:00:00+00:008
 
7.0%
2020-12-24T09:00:00+00:005
 
4.3%
2020-12-24T03:00:00+00:003
 
2.6%
2020-12-24T05:00:00+00:003
 
2.6%
2020-12-24T10:00:00+00:002
 
1.7%
2020-12-24T08:00:00+00:002
 
1.7%
Other values (9)12
 
10.4%

Length

2022-05-09T21:20:33.685322image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-12-24t12:00:00+00:0033
28.7%
2020-12-24t04:00:00+00:0024
20.9%
2020-12-24t06:30:00+00:0014
12.2%
2020-12-24t17:00:00+00:009
 
7.8%
2020-12-24t00:00:00+00:008
 
7.0%
2020-12-24t09:00:00+00:005
 
4.3%
2020-12-24t03:00:00+00:003
 
2.6%
2020-12-24t05:00:00+00:003
 
2.6%
2020-12-24t13:00:00+00:002
 
1.7%
2020-12-24t14:00:00+00:002
 
1.7%
Other values (9)12
 
10.4%

Most occurring characters

ValueCountFrequency (%)
01204
41.9%
2498
17.3%
:345
 
12.0%
-230
 
8.0%
1170
 
5.9%
4141
 
4.9%
T115
 
4.0%
+115
 
4.0%
320
 
0.7%
614
 
0.5%
Other values (4)23
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2070
72.0%
Other Punctuation345
 
12.0%
Dash Punctuation230
 
8.0%
Uppercase Letter115
 
4.0%
Math Symbol115
 
4.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01204
58.2%
2498
24.1%
1170
 
8.2%
4141
 
6.8%
320
 
1.0%
614
 
0.7%
79
 
0.4%
96
 
0.3%
55
 
0.2%
83
 
0.1%
Other Punctuation
ValueCountFrequency (%)
:345
100.0%
Dash Punctuation
ValueCountFrequency (%)
-230
100.0%
Uppercase Letter
ValueCountFrequency (%)
T115
100.0%
Math Symbol
ValueCountFrequency (%)
+115
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2760
96.0%
Latin115
 
4.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01204
43.6%
2498
18.0%
:345
 
12.5%
-230
 
8.3%
1170
 
6.2%
4141
 
5.1%
+115
 
4.2%
320
 
0.7%
614
 
0.5%
79
 
0.3%
Other values (3)14
 
0.5%
Latin
ValueCountFrequency (%)
T115
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2875
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01204
41.9%
2498
17.3%
:345
 
12.0%
-230
 
8.0%
1170
 
5.9%
4141
 
4.9%
T115
 
4.0%
+115
 
4.0%
320
 
0.7%
614
 
0.5%
Other values (4)23
 
0.8%

runtime
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct38
Distinct (%)36.9%
Missing12
Missing (%)10.4%
Infinite0
Infinite (%)0.0%
Mean37.95145631
Minimum4
Maximum171
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2022-05-09T21:20:33.763769image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile10
Q121.5
median42
Q345.5
95-th percentile64.5
Maximum171
Range167
Interquartile range (IQR)24

Descriptive statistics

Standard deviation21.5858426
Coefficient of variation (CV)0.56877508
Kurtosis12.91866609
Mean37.95145631
Median Absolute Deviation (MAD)12
Skewness2.196419659
Sum3909
Variance465.9486008
MonotonicityNot monotonic
2022-05-09T21:20:33.896456image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
4525
21.7%
507
 
6.1%
207
 
6.1%
126
 
5.2%
306
 
5.2%
605
 
4.3%
385
 
4.3%
103
 
2.6%
72
 
1.7%
232
 
1.7%
Other values (28)35
30.4%
(Missing)12
 
10.4%
ValueCountFrequency (%)
41
 
0.9%
62
 
1.7%
72
 
1.7%
103
2.6%
111
 
0.9%
126
5.2%
172
 
1.7%
181
 
0.9%
207
6.1%
211
 
0.9%
ValueCountFrequency (%)
1711
 
0.9%
781
 
0.9%
771
 
0.9%
701
 
0.9%
671
 
0.9%
651
 
0.9%
605
4.3%
551
 
0.9%
542
 
1.7%
531
 
0.9%

summary
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct34
Distinct (%)29.6%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
nan
82 
<p>In this week's installment of "The Ready Room," host Wil Wheaton (Star Trek: The Next Generation) is joined by star Doug Jones (Captain Saru), who explains the intricacies of working in prosthetics and his thoughts on Saru's journey this season. Then, Janet Kidder (Osyraa) speaks with Wil about joining the Star Trek Universe's esteemed villains and the thrill of hijacking a Starfleet ship.</p>
 
1
<p>As Chris and Harris set out to live off the land, the Off the Cuff crew learns about a controversial mine, the history of the BWCAW and the community of Ely, MN. A Boundary Waters documentary.</p>
 
1
<p>Ely, MN will leave behind either a greener economy or a darker environment. Chris, Harris and the Off the Cuff crew explore a highly controversial mine, as well as attempt to complete their four day journey in the wild. A Boundary Waters documentary.</p>
 
1
<p>Point Roberts, WA may be sold to Canada. Chris and Harris explore a unique mapping accident and the fascinating community that came from it. A Point Roberts documentary.</p>
 
1
Other values (29)
29 

Length

Max length540
Median length3
Mean length63.99130435
Min length3

Characters and Unicode

Total characters7359
Distinct characters64
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)28.7%

Sample

1st rownan
2nd rownan
3rd rownan
4th rownan
5th rownan

Common Values

ValueCountFrequency (%)
nan82
71.3%
<p>In this week's installment of "The Ready Room," host Wil Wheaton (Star Trek: The Next Generation) is joined by star Doug Jones (Captain Saru), who explains the intricacies of working in prosthetics and his thoughts on Saru's journey this season. Then, Janet Kidder (Osyraa) speaks with Wil about joining the Star Trek Universe's esteemed villains and the thrill of hijacking a Starfleet ship.</p>1
 
0.9%
<p>As Chris and Harris set out to live off the land, the Off the Cuff crew learns about a controversial mine, the history of the BWCAW and the community of Ely, MN. A Boundary Waters documentary.</p>1
 
0.9%
<p>Ely, MN will leave behind either a greener economy or a darker environment. Chris, Harris and the Off the Cuff crew explore a highly controversial mine, as well as attempt to complete their four day journey in the wild. A Boundary Waters documentary.</p>1
 
0.9%
<p>Point Roberts, WA may be sold to Canada. Chris and Harris explore a unique mapping accident and the fascinating community that came from it. A Point Roberts documentary.</p>1
 
0.9%
<p>The Nebula-75 crew are all set to make the best of a Christmas far from home, but when they cross paths with a stranded vessel a tale of treacherous trickery reveals itself...</p>1
 
0.9%
<p>The events of the previous night left a sour taste in James's mouth, much to Dale's chagrin. But something's got to give.</p>1
 
0.9%
<p>Ellen introduces Omar to the family. Anderson is bothered by Tina's strange posts on the internet. Lica goes back to talking to Samantha. Keyla makes a revelation to Samuel.</p>1
 
0.9%
<p>Oscar has to distract Lucy while the Yetis prepare a surprise for her.</p>1
 
0.9%
<p>Eminent lawyer Bikram Chandra's happy life takes a nosedive when his wife, Anuradha, stabs him. Cops are baffled with her, and finding a lawyer for her looks impossible.</p>1
 
0.9%
Other values (24)24
 
20.9%

Length

2022-05-09T21:20:33.998599image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nan82
 
6.5%
the79
 
6.3%
a42
 
3.3%
to40
 
3.2%
and40
 
3.2%
of27
 
2.1%
his14
 
1.1%
in12
 
1.0%
on11
 
0.9%
for10
 
0.8%
Other values (528)899
71.6%

Most occurring characters

ValueCountFrequency (%)
1137
15.5%
e677
 
9.2%
a579
 
7.9%
n550
 
7.5%
t477
 
6.5%
s406
 
5.5%
i391
 
5.3%
o333
 
4.5%
h318
 
4.3%
r302
 
4.1%
Other values (54)2189
29.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter5565
75.6%
Space Separator1141
 
15.5%
Uppercase Letter270
 
3.7%
Other Punctuation224
 
3.0%
Math Symbol144
 
2.0%
Dash Punctuation7
 
0.1%
Close Punctuation3
 
< 0.1%
Open Punctuation3
 
< 0.1%
Decimal Number2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e677
12.2%
a579
10.4%
n550
9.9%
t477
 
8.6%
s406
 
7.3%
i391
 
7.0%
o333
 
6.0%
h318
 
5.7%
r302
 
5.4%
d189
 
3.4%
Other values (16)1343
24.1%
Uppercase Letter
ValueCountFrequency (%)
M34
 
12.6%
A20
 
7.4%
C20
 
7.4%
B19
 
7.0%
T18
 
6.7%
W16
 
5.9%
F15
 
5.6%
S15
 
5.6%
N13
 
4.8%
R13
 
4.8%
Other values (13)87
32.2%
Other Punctuation
ValueCountFrequency (%)
.81
36.2%
,77
34.4%
/36
16.1%
'21
 
9.4%
"8
 
3.6%
:1
 
0.4%
Space Separator
ValueCountFrequency (%)
1137
99.6%
 4
 
0.4%
Math Symbol
ValueCountFrequency (%)
<72
50.0%
>72
50.0%
Decimal Number
ValueCountFrequency (%)
71
50.0%
51
50.0%
Dash Punctuation
ValueCountFrequency (%)
-7
100.0%
Close Punctuation
ValueCountFrequency (%)
)3
100.0%
Open Punctuation
ValueCountFrequency (%)
(3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin5835
79.3%
Common1524
 
20.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e677
11.6%
a579
 
9.9%
n550
 
9.4%
t477
 
8.2%
s406
 
7.0%
i391
 
6.7%
o333
 
5.7%
h318
 
5.4%
r302
 
5.2%
d189
 
3.2%
Other values (39)1613
27.6%
Common
ValueCountFrequency (%)
1137
74.6%
.81
 
5.3%
,77
 
5.1%
<72
 
4.7%
>72
 
4.7%
/36
 
2.4%
'21
 
1.4%
"8
 
0.5%
-7
 
0.5%
 4
 
0.3%
Other values (5)9
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII7355
99.9%
None4
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1137
15.5%
e677
 
9.2%
a579
 
7.9%
n550
 
7.5%
t477
 
6.5%
s406
 
5.5%
i391
 
5.3%
o333
 
4.5%
h318
 
4.3%
r302
 
4.1%
Other values (53)2185
29.7%
None
ValueCountFrequency (%)
 4
100.0%

_embedded_show_id
Real number (ℝ≥0)

HIGH CORRELATION

Distinct72
Distinct (%)62.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47802.50435
Minimum2504
Maximum60848
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2022-05-09T21:20:34.129777image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2504
5-th percentile26643
Q145526
median52499
Q352791.5
95-th percentile58463.6
Maximum60848
Range58344
Interquartile range (IQR)7265.5

Descriptive statistics

Standard deviation10664.77208
Coefficient of variation (CV)0.2231006978
Kurtosis5.62557787
Mean47802.50435
Median Absolute Deviation (MAD)2111
Skewness-2.238187847
Sum5497288
Variance113737363.5
MonotonicityNot monotonic
2022-05-09T21:20:34.245464image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
526098
 
7.0%
526106
 
5.2%
414906
 
5.2%
527846
 
5.2%
266435
 
4.3%
533193
 
2.6%
521812
 
1.7%
498432
 
1.7%
481512
 
1.7%
551992
 
1.7%
Other values (62)73
63.5%
ValueCountFrequency (%)
25041
 
0.9%
65441
 
0.9%
74801
 
0.9%
167531
 
0.9%
266435
4.3%
283811
 
0.9%
306061
 
0.9%
357901
 
0.9%
380311
 
0.9%
390531
 
0.9%
ValueCountFrequency (%)
608482
1.7%
600861
0.9%
592611
0.9%
586892
1.7%
583671
0.9%
575561
0.9%
566051
0.9%
562531
0.9%
551992
1.7%
546581
0.9%

_embedded_show_url
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct72
Distinct (%)62.6%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
https://www.tvmaze.com/shows/52609/criminal-justice-behind-closed-doors
 
8
https://www.tvmaze.com/shows/52610/feluda-pherot
 
6
https://www.tvmaze.com/shows/41490/unique-lady
 
6
https://www.tvmaze.com/shows/52784/unique-lady-2
 
6
https://www.tvmaze.com/shows/26643/summer-camp-island
 
5
Other values (67)
84 

Length

Max length83
Median length59
Mean length50.46956522
Min length39

Characters and Unicode

Total characters5804
Distinct characters40
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique51 ?
Unique (%)44.3%

Sample

1st rowhttps://www.tvmaze.com/shows/39115/obycnaa-zensina
2nd rowhttps://www.tvmaze.com/shows/43722/257-pricin-ctoby-zit
3rd rowhttps://www.tvmaze.com/shows/48151/smesariki-novyj-sezon
4th rowhttps://www.tvmaze.com/shows/48151/smesariki-novyj-sezon
5th rowhttps://www.tvmaze.com/shows/49280/psih

Common Values

ValueCountFrequency (%)
https://www.tvmaze.com/shows/52609/criminal-justice-behind-closed-doors8
 
7.0%
https://www.tvmaze.com/shows/52610/feluda-pherot6
 
5.2%
https://www.tvmaze.com/shows/41490/unique-lady6
 
5.2%
https://www.tvmaze.com/shows/52784/unique-lady-26
 
5.2%
https://www.tvmaze.com/shows/26643/summer-camp-island5
 
4.3%
https://www.tvmaze.com/shows/53319/off-the-cuff3
 
2.6%
https://www.tvmaze.com/shows/52181/volk2
 
1.7%
https://www.tvmaze.com/shows/49843/aile-sirketi2
 
1.7%
https://www.tvmaze.com/shows/48151/smesariki-novyj-sezon2
 
1.7%
https://www.tvmaze.com/shows/55199/klassen2
 
1.7%
Other values (62)73
63.5%

Length

2022-05-09T21:20:34.368841image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://www.tvmaze.com/shows/52609/criminal-justice-behind-closed-doors8
 
7.0%
https://www.tvmaze.com/shows/41490/unique-lady6
 
5.2%
https://www.tvmaze.com/shows/52784/unique-lady-26
 
5.2%
https://www.tvmaze.com/shows/52610/feluda-pherot6
 
5.2%
https://www.tvmaze.com/shows/26643/summer-camp-island5
 
4.3%
https://www.tvmaze.com/shows/53319/off-the-cuff3
 
2.6%
https://www.tvmaze.com/shows/52421/you-complete-me2
 
1.7%
https://www.tvmaze.com/shows/52780/mermaid-prince2
 
1.7%
https://www.tvmaze.com/shows/53830/witches2
 
1.7%
https://www.tvmaze.com/shows/60848/blippi2
 
1.7%
Other values (62)73
63.5%

Most occurring characters

ValueCountFrequency (%)
/575
 
9.9%
w478
 
8.2%
s457
 
7.9%
t441
 
7.6%
o350
 
6.0%
e301
 
5.2%
m295
 
5.1%
h274
 
4.7%
.230
 
4.0%
a228
 
3.9%
Other values (30)2175
37.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4108
70.8%
Other Punctuation920
 
15.9%
Decimal Number588
 
10.1%
Dash Punctuation188
 
3.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
w478
11.6%
s457
11.1%
t441
10.7%
o350
 
8.5%
e301
 
7.3%
m295
 
7.2%
h274
 
6.7%
a228
 
5.6%
c174
 
4.2%
p153
 
3.7%
Other values (16)957
23.3%
Decimal Number
ValueCountFrequency (%)
595
16.2%
276
12.9%
470
11.9%
661
10.4%
057
9.7%
956
9.5%
851
8.7%
150
8.5%
341
7.0%
731
 
5.3%
Other Punctuation
ValueCountFrequency (%)
/575
62.5%
.230
 
25.0%
:115
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
-188
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4108
70.8%
Common1696
29.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
w478
11.6%
s457
11.1%
t441
10.7%
o350
 
8.5%
e301
 
7.3%
m295
 
7.2%
h274
 
6.7%
a228
 
5.6%
c174
 
4.2%
p153
 
3.7%
Other values (16)957
23.3%
Common
ValueCountFrequency (%)
/575
33.9%
.230
 
13.6%
-188
 
11.1%
:115
 
6.8%
595
 
5.6%
276
 
4.5%
470
 
4.1%
661
 
3.6%
057
 
3.4%
956
 
3.3%
Other values (4)173
 
10.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII5804
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/575
 
9.9%
w478
 
8.2%
s457
 
7.9%
t441
 
7.6%
o350
 
6.0%
e301
 
5.2%
m295
 
5.1%
h274
 
4.7%
.230
 
4.0%
a228
 
3.9%
Other values (30)2175
37.5%

_embedded_show_name
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct72
Distinct (%)62.6%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
Criminal Justice: Behind Closed Doors
 
8
Feluda Pherot
 
6
Unique Lady
 
6
Unique Lady 2
 
6
Summer Camp Island
 
5
Other values (67)
84 

Length

Max length50
Median length28
Mean length15.76521739
Min length4

Characters and Unicode

Total characters1813
Distinct characters108
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique51 ?
Unique (%)44.3%

Sample

1st rowОбычная женщина
2nd row257 причин, чтобы жить
3rd rowСмешарики. Новый сезон
4th rowСмешарики. Новый сезон
5th rowПсих

Common Values

ValueCountFrequency (%)
Criminal Justice: Behind Closed Doors8
 
7.0%
Feluda Pherot6
 
5.2%
Unique Lady6
 
5.2%
Unique Lady 26
 
5.2%
Summer Camp Island5
 
4.3%
Off the Cuff3
 
2.6%
Волк2
 
1.7%
Aile Şirketi2
 
1.7%
Смешарики. Новый сезон2
 
1.7%
Klassen2
 
1.7%
Other values (62)73
63.5%

Length

2022-05-09T21:20:34.466917image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
lady12
 
4.0%
the12
 
4.0%
unique12
 
4.0%
doors8
 
2.6%
criminal8
 
2.6%
closed8
 
2.6%
behind8
 
2.6%
justice8
 
2.6%
feluda6
 
2.0%
pherot6
 
2.0%
Other values (153)215
71.0%

Most occurring characters

ValueCountFrequency (%)
188
 
10.4%
e170
 
9.4%
i94
 
5.2%
o90
 
5.0%
a89
 
4.9%
n79
 
4.4%
r75
 
4.1%
s68
 
3.8%
t67
 
3.7%
l63
 
3.5%
Other values (98)830
45.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1300
71.7%
Uppercase Letter281
 
15.5%
Space Separator188
 
10.4%
Other Punctuation27
 
1.5%
Decimal Number16
 
0.9%
Dash Punctuation1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e170
 
13.1%
i94
 
7.2%
o90
 
6.9%
a89
 
6.8%
n79
 
6.1%
r75
 
5.8%
s68
 
5.2%
t67
 
5.2%
l63
 
4.8%
d58
 
4.5%
Other values (48)447
34.4%
Uppercase Letter
ValueCountFrequency (%)
C28
 
10.0%
S27
 
9.6%
T24
 
8.5%
L22
 
7.8%
M18
 
6.4%
U16
 
5.7%
B15
 
5.3%
P14
 
5.0%
J10
 
3.6%
W10
 
3.6%
Other values (26)97
34.5%
Other Punctuation
ValueCountFrequency (%)
:11
40.7%
'5
18.5%
.4
 
14.8%
,3
 
11.1%
!2
 
7.4%
?1
 
3.7%
&1
 
3.7%
Decimal Number
ValueCountFrequency (%)
29
56.2%
02
 
12.5%
72
 
12.5%
52
 
12.5%
61
 
6.2%
Space Separator
ValueCountFrequency (%)
188
100.0%
Dash Punctuation
ValueCountFrequency (%)
-1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1405
77.5%
Common232
 
12.8%
Cyrillic176
 
9.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e170
 
12.1%
i94
 
6.7%
o90
 
6.4%
a89
 
6.3%
n79
 
5.6%
r75
 
5.3%
s68
 
4.8%
t67
 
4.8%
l63
 
4.5%
d58
 
4.1%
Other values (46)552
39.3%
Cyrillic
ValueCountFrequency (%)
о21
 
11.9%
и13
 
7.4%
р12
 
6.8%
а12
 
6.8%
н11
 
6.2%
е11
 
6.2%
с8
 
4.5%
к7
 
4.0%
ы6
 
3.4%
т5
 
2.8%
Other values (28)70
39.8%
Common
ValueCountFrequency (%)
188
81.0%
:11
 
4.7%
29
 
3.9%
'5
 
2.2%
.4
 
1.7%
,3
 
1.3%
02
 
0.9%
72
 
0.9%
52
 
0.9%
!2
 
0.9%
Other values (4)4
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1629
89.9%
Cyrillic176
 
9.7%
None8
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
188
 
11.5%
e170
 
10.4%
i94
 
5.8%
o90
 
5.5%
a89
 
5.5%
n79
 
4.8%
r75
 
4.6%
s68
 
4.2%
t67
 
4.1%
l63
 
3.9%
Other values (54)646
39.7%
Cyrillic
ValueCountFrequency (%)
о21
 
11.9%
и13
 
7.4%
р12
 
6.8%
а12
 
6.8%
н11
 
6.2%
е11
 
6.2%
с8
 
4.5%
к7
 
4.0%
ы6
 
3.4%
т5
 
2.8%
Other values (28)70
39.8%
None
ValueCountFrequency (%)
ı2
25.0%
Ş2
25.0%
ė1
12.5%
Ç1
12.5%
ğ1
12.5%
ø1
12.5%

_embedded_show_type
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct7
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
Scripted
77 
Animation
12 
Talk Show
11 
Documentary
 
7
Reality
 
4
Other values (2)
 
4

Length

Max length11
Median length8
Mean length8.304347826
Min length6

Characters and Unicode

Total characters955
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.9%

Sample

1st rowScripted
2nd rowScripted
3rd rowAnimation
4th rowAnimation
5th rowScripted

Common Values

ValueCountFrequency (%)
Scripted77
67.0%
Animation12
 
10.4%
Talk Show11
 
9.6%
Documentary7
 
6.1%
Reality4
 
3.5%
Variety3
 
2.6%
Sports1
 
0.9%

Length

2022-05-09T21:20:34.561122image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T21:20:34.670800image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
scripted77
61.1%
animation12
 
9.5%
talk11
 
8.7%
show11
 
8.7%
documentary7
 
5.6%
reality4
 
3.2%
variety3
 
2.4%
sports1
 
0.8%

Most occurring characters

ValueCountFrequency (%)
i108
11.3%
t104
10.9%
e91
9.5%
S89
9.3%
r88
9.2%
c84
8.8%
p78
8.2%
d77
8.1%
a37
 
3.9%
o31
 
3.2%
Other values (15)168
17.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter818
85.7%
Uppercase Letter126
 
13.2%
Space Separator11
 
1.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i108
13.2%
t104
12.7%
e91
11.1%
r88
10.8%
c84
10.3%
p78
9.5%
d77
9.4%
a37
 
4.5%
o31
 
3.8%
n31
 
3.8%
Other values (8)89
10.9%
Uppercase Letter
ValueCountFrequency (%)
S89
70.6%
A12
 
9.5%
T11
 
8.7%
D7
 
5.6%
R4
 
3.2%
V3
 
2.4%
Space Separator
ValueCountFrequency (%)
11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin944
98.8%
Common11
 
1.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
i108
11.4%
t104
11.0%
e91
9.6%
S89
9.4%
r88
9.3%
c84
8.9%
p78
8.3%
d77
8.2%
a37
 
3.9%
o31
 
3.3%
Other values (14)157
16.6%
Common
ValueCountFrequency (%)
11
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII955
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i108
11.3%
t104
10.9%
e91
9.5%
S89
9.3%
r88
9.2%
c84
8.8%
p78
8.2%
d77
8.1%
a37
 
3.9%
o31
 
3.2%
Other values (15)168
17.6%

_embedded_show_language
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct18
Distinct (%)15.7%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
Chinese
32 
English
23 
Russian
15 
Korean
Hindi
Other values (13)
29 

Length

Max length10
Median length7
Mean length6.72173913
Min length3

Characters and Unicode

Total characters773
Distinct characters33
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)3.5%

Sample

1st rowRussian
2nd rowRussian
3rd rowRussian
4th rowRussian
5th rowRussian

Common Values

ValueCountFrequency (%)
Chinese32
27.8%
English23
20.0%
Russian15
13.0%
Korean8
 
7.0%
Hindi8
 
7.0%
Bengali6
 
5.2%
Norwegian3
 
2.6%
Swedish3
 
2.6%
Turkish3
 
2.6%
nan2
 
1.7%
Other values (8)12
 
10.4%

Length

2022-05-09T21:20:34.780371image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
chinese32
27.8%
english23
20.0%
russian15
13.0%
korean8
 
7.0%
hindi8
 
7.0%
bengali6
 
5.2%
norwegian3
 
2.6%
swedish3
 
2.6%
turkish3
 
2.6%
arabic2
 
1.7%
Other values (8)12
 
10.4%

Most occurring characters

ValueCountFrequency (%)
i109
14.1%
n103
13.3%
s92
11.9%
e86
11.1%
h66
8.5%
a48
 
6.2%
g37
 
4.8%
C32
 
4.1%
l32
 
4.1%
E23
 
3.0%
Other values (23)145
18.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter660
85.4%
Uppercase Letter113
 
14.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i109
16.5%
n103
15.6%
s92
13.9%
e86
13.0%
h66
10.0%
a48
7.3%
g37
 
5.6%
l32
 
4.8%
u23
 
3.5%
r18
 
2.7%
Other values (8)46
7.0%
Uppercase Letter
ValueCountFrequency (%)
C32
28.3%
E23
20.4%
R15
13.3%
H8
 
7.1%
K8
 
7.1%
T7
 
6.2%
B6
 
5.3%
N3
 
2.7%
S3
 
2.7%
D2
 
1.8%
Other values (5)6
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
Latin773
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i109
14.1%
n103
13.3%
s92
11.9%
e86
11.1%
h66
8.5%
a48
 
6.2%
g37
 
4.8%
C32
 
4.1%
l32
 
4.1%
E23
 
3.0%
Other values (23)145
18.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII773
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i109
14.1%
n103
13.3%
s92
11.9%
e86
11.1%
h66
8.5%
a48
 
6.2%
g37
 
4.8%
C32
 
4.1%
l32
 
4.1%
E23
 
3.0%
Other values (23)145
18.8%

_embedded_show_genres
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct31
Distinct (%)27.0%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
[]
19 
['Drama', 'Romance']
12 
['Comedy']
['Crime']
['Comedy', 'Fantasy', 'Romance']
Other values (26)
60 

Length

Max length42
Median length35
Mean length19.60869565
Min length2

Characters and Unicode

Total characters2255
Distinct characters32
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)10.4%

Sample

1st row['Drama', 'Crime', 'Mystery']
2nd row['Drama', 'Comedy']
3rd row['Comedy', 'Family']
4th row['Comedy', 'Family']
5th row['Drama', 'Thriller']

Common Values

ValueCountFrequency (%)
[]19
16.5%
['Drama', 'Romance']12
 
10.4%
['Comedy']8
 
7.0%
['Crime']8
 
7.0%
['Comedy', 'Fantasy', 'Romance']8
 
7.0%
['Drama', 'Comedy', 'Romance']7
 
6.1%
['Drama', 'Thriller', 'Mystery']6
 
5.2%
['Comedy', 'Adventure', 'Fantasy']5
 
4.3%
['Sports']4
 
3.5%
['Drama', 'Action', 'Crime']4
 
3.5%
Other values (21)34
29.6%

Length

2022-05-09T21:20:34.899036image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
drama49
21.2%
comedy38
16.5%
romance31
13.4%
19
 
8.2%
crime15
 
6.5%
fantasy15
 
6.5%
adventure11
 
4.8%
mystery11
 
4.8%
action7
 
3.0%
thriller7
 
3.0%
Other values (10)28
12.1%

Most occurring characters

ValueCountFrequency (%)
'424
18.8%
a168
 
7.5%
e142
 
6.3%
m139
 
6.2%
r120
 
5.3%
,116
 
5.1%
116
 
5.1%
[115
 
5.1%
]115
 
5.1%
o93
 
4.1%
Other values (22)707
31.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1149
51.0%
Other Punctuation540
23.9%
Uppercase Letter216
 
9.6%
Space Separator116
 
5.1%
Open Punctuation115
 
5.1%
Close Punctuation115
 
5.1%
Dash Punctuation4
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a168
14.6%
e142
12.4%
m139
12.1%
r120
10.4%
o93
8.1%
n82
7.1%
y81
7.0%
t58
 
5.0%
d55
 
4.8%
i55
 
4.8%
Other values (7)156
13.6%
Uppercase Letter
ValueCountFrequency (%)
C57
26.4%
D49
22.7%
R31
14.4%
F24
11.1%
A21
 
9.7%
M12
 
5.6%
S11
 
5.1%
T7
 
3.2%
H4
 
1.9%
Other Punctuation
ValueCountFrequency (%)
'424
78.5%
,116
 
21.5%
Space Separator
ValueCountFrequency (%)
116
100.0%
Open Punctuation
ValueCountFrequency (%)
[115
100.0%
Close Punctuation
ValueCountFrequency (%)
]115
100.0%
Dash Punctuation
ValueCountFrequency (%)
-4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1365
60.5%
Common890
39.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a168
12.3%
e142
 
10.4%
m139
 
10.2%
r120
 
8.8%
o93
 
6.8%
n82
 
6.0%
y81
 
5.9%
t58
 
4.2%
C57
 
4.2%
d55
 
4.0%
Other values (16)370
27.1%
Common
ValueCountFrequency (%)
'424
47.6%
,116
 
13.0%
116
 
13.0%
[115
 
12.9%
]115
 
12.9%
-4
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII2255
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
'424
18.8%
a168
 
7.5%
e142
 
6.3%
m139
 
6.2%
r120
 
5.3%
,116
 
5.1%
116
 
5.1%
[115
 
5.1%
]115
 
5.1%
o93
 
4.1%
Other values (22)707
31.4%

_embedded_show_status
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
Running
55 
Ended
50 
To Be Determined
10 

Length

Max length16
Median length7
Mean length6.913043478
Min length5

Characters and Unicode

Total characters795
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEnded
2nd rowEnded
3rd rowRunning
4th rowRunning
5th rowEnded

Common Values

ValueCountFrequency (%)
Running55
47.8%
Ended50
43.5%
To Be Determined10
 
8.7%

Length

2022-05-09T21:20:35.089731image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T21:20:35.184303image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
running55
40.7%
ended50
37.0%
to10
 
7.4%
be10
 
7.4%
determined10
 
7.4%

Most occurring characters

ValueCountFrequency (%)
n225
28.3%
d110
13.8%
e90
 
11.3%
i65
 
8.2%
R55
 
6.9%
u55
 
6.9%
g55
 
6.9%
E50
 
6.3%
20
 
2.5%
T10
 
1.3%
Other values (6)60
 
7.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter640
80.5%
Uppercase Letter135
 
17.0%
Space Separator20
 
2.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n225
35.2%
d110
17.2%
e90
 
14.1%
i65
 
10.2%
u55
 
8.6%
g55
 
8.6%
o10
 
1.6%
t10
 
1.6%
r10
 
1.6%
m10
 
1.6%
Uppercase Letter
ValueCountFrequency (%)
R55
40.7%
E50
37.0%
T10
 
7.4%
B10
 
7.4%
D10
 
7.4%
Space Separator
ValueCountFrequency (%)
20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin775
97.5%
Common20
 
2.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
n225
29.0%
d110
14.2%
e90
 
11.6%
i65
 
8.4%
R55
 
7.1%
u55
 
7.1%
g55
 
7.1%
E50
 
6.5%
T10
 
1.3%
o10
 
1.3%
Other values (5)50
 
6.5%
Common
ValueCountFrequency (%)
20
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII795
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n225
28.3%
d110
13.8%
e90
 
11.3%
i65
 
8.2%
R55
 
6.9%
u55
 
6.9%
g55
 
6.9%
E50
 
6.3%
20
 
2.5%
T10
 
1.3%
Other values (6)60
 
7.5%

_embedded_show_runtime
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct21
Distinct (%)27.3%
Missing38
Missing (%)33.0%
Infinite0
Infinite (%)0.0%
Mean36.64935065
Minimum4
Maximum62
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2022-05-09T21:20:35.276246image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile9.6
Q122
median45
Q345
95-th percentile60
Maximum62
Range58
Interquartile range (IQR)23

Descriptive statistics

Standard deviation15.43301846
Coefficient of variation (CV)0.4210993697
Kurtosis-0.7738679664
Mean36.64935065
Median Absolute Deviation (MAD)7
Skewness-0.4499650578
Sum2822
Variance238.1780588
MonotonicityNot monotonic
2022-05-09T21:20:35.374435image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
4528
24.3%
208
 
7.0%
386
 
5.2%
606
 
5.2%
305
 
4.3%
104
 
3.5%
252
 
1.7%
502
 
1.7%
222
 
1.7%
512
 
1.7%
Other values (11)12
 
10.4%
(Missing)38
33.0%
ValueCountFrequency (%)
41
 
0.9%
72
 
1.7%
81
 
0.9%
104
3.5%
131
 
0.9%
181
 
0.9%
208
7.0%
222
 
1.7%
231
 
0.9%
252
 
1.7%
ValueCountFrequency (%)
621
 
0.9%
606
 
5.2%
581
 
0.9%
531
 
0.9%
512
 
1.7%
502
 
1.7%
4528
24.3%
401
 
0.9%
386
 
5.2%
351
 
0.9%

_embedded_show_averageRuntime
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct39
Distinct (%)37.1%
Missing10
Missing (%)8.7%
Infinite0
Infinite (%)0.0%
Mean35.9047619
Minimum2
Maximum77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2022-05-09T21:20:35.477200image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile9.2
Q121
median42
Q345
95-th percentile60
Maximum77
Range75
Interquartile range (IQR)24

Descriptive statistics

Standard deviation17.39336954
Coefficient of variation (CV)0.4844307167
Kurtosis-0.8270300923
Mean35.9047619
Median Absolute Deviation (MAD)12
Skewness-0.1550161109
Sum3770
Variance302.529304
MonotonicityNot monotonic
2022-05-09T21:20:35.585664image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
4526
22.6%
5010
 
8.7%
426
 
5.2%
105
 
4.3%
605
 
4.3%
115
 
4.3%
304
 
3.5%
204
 
3.5%
573
 
2.6%
213
 
2.6%
Other values (29)34
29.6%
(Missing)10
 
8.7%
ValueCountFrequency (%)
21
 
0.9%
41
 
0.9%
72
 
1.7%
81
 
0.9%
91
 
0.9%
105
4.3%
115
4.3%
121
 
0.9%
131
 
0.9%
141
 
0.9%
ValueCountFrequency (%)
771
 
0.9%
761
 
0.9%
631
 
0.9%
621
 
0.9%
605
4.3%
573
 
2.6%
561
 
0.9%
531
 
0.9%
5010
8.7%
481
 
0.9%

_embedded_show_premiered
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct54
Distinct (%)47.0%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
2020-12-24
24 
2019-01-17
 
6
2018-07-07
 
5
2020-12-10
 
5
2020-12-17
 
5
Other values (49)
70 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1150
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique34 ?
Unique (%)29.6%

Sample

1st row2018-10-29
2nd row2020-03-26
3rd row2020-05-18
4th row2020-05-18
5th row2020-11-05

Common Values

ValueCountFrequency (%)
2020-12-2424
20.9%
2019-01-176
 
5.2%
2018-07-075
 
4.3%
2020-12-105
 
4.3%
2020-12-175
 
4.3%
2020-12-033
 
2.6%
2019-04-303
 
2.6%
2020-08-063
 
2.6%
2020-11-123
 
2.6%
2020-11-263
 
2.6%
Other values (44)55
47.8%

Length

2022-05-09T21:20:35.679700image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-12-2424
20.9%
2019-01-176
 
5.2%
2018-07-075
 
4.3%
2020-12-105
 
4.3%
2020-12-175
 
4.3%
2020-12-033
 
2.6%
2019-04-303
 
2.6%
2020-08-063
 
2.6%
2020-11-123
 
2.6%
2020-11-263
 
2.6%
Other values (44)55
47.8%

Most occurring characters

ValueCountFrequency (%)
2290
25.2%
0282
24.5%
-230
20.0%
1185
16.1%
436
 
3.1%
730
 
2.6%
926
 
2.3%
823
 
2.0%
321
 
1.8%
618
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number920
80.0%
Dash Punctuation230
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2290
31.5%
0282
30.7%
1185
20.1%
436
 
3.9%
730
 
3.3%
926
 
2.8%
823
 
2.5%
321
 
2.3%
618
 
2.0%
59
 
1.0%
Dash Punctuation
ValueCountFrequency (%)
-230
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1150
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2290
25.2%
0282
24.5%
-230
20.0%
1185
16.1%
436
 
3.1%
730
 
2.6%
926
 
2.3%
823
 
2.0%
321
 
1.8%
618
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1150
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2290
25.2%
0282
24.5%
-230
20.0%
1185
16.1%
436
 
3.1%
730
 
2.6%
926
 
2.3%
823
 
2.0%
321
 
1.8%
618
 
1.6%

_embedded_show_ended
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct23
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
nan
65 
2020-12-24
13 
2021-01-07
2021-01-14
 
4
2020-12-28
 
2
Other values (18)
24 

Length

Max length10
Median length3
Mean length6.043478261
Min length3

Characters and Unicode

Total characters695
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)10.4%

Sample

1st row2021-01-07
2nd row2021-01-21
3rd rownan
4th rownan
5th row2020-12-24

Common Values

ValueCountFrequency (%)
nan65
56.5%
2020-12-2413
 
11.3%
2021-01-077
 
6.1%
2021-01-144
 
3.5%
2020-12-282
 
1.7%
2021-01-042
 
1.7%
2021-01-282
 
1.7%
2021-01-022
 
1.7%
2020-12-302
 
1.7%
2021-10-072
 
1.7%
Other values (13)14
 
12.2%

Length

2022-05-09T21:20:35.773482image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nan65
56.5%
2020-12-2413
 
11.3%
2021-01-077
 
6.1%
2021-01-144
 
3.5%
2020-12-282
 
1.7%
2021-01-042
 
1.7%
2021-01-282
 
1.7%
2021-01-022
 
1.7%
2020-12-302
 
1.7%
2021-10-072
 
1.7%
Other values (13)14
 
12.2%

Most occurring characters

ValueCountFrequency (%)
2150
21.6%
n130
18.7%
0117
16.8%
-100
14.4%
187
12.5%
a65
9.4%
420
 
2.9%
712
 
1.7%
85
 
0.7%
64
 
0.6%
Other values (2)5
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number400
57.6%
Lowercase Letter195
28.1%
Dash Punctuation100
 
14.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2150
37.5%
0117
29.2%
187
21.8%
420
 
5.0%
712
 
3.0%
85
 
1.2%
64
 
1.0%
33
 
0.8%
52
 
0.5%
Lowercase Letter
ValueCountFrequency (%)
n130
66.7%
a65
33.3%
Dash Punctuation
ValueCountFrequency (%)
-100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common500
71.9%
Latin195
 
28.1%

Most frequent character per script

Common
ValueCountFrequency (%)
2150
30.0%
0117
23.4%
-100
20.0%
187
17.4%
420
 
4.0%
712
 
2.4%
85
 
1.0%
64
 
0.8%
33
 
0.6%
52
 
0.4%
Latin
ValueCountFrequency (%)
n130
66.7%
a65
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII695
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2150
21.6%
n130
18.7%
0117
16.8%
-100
14.4%
187
12.5%
a65
9.4%
420
 
2.9%
712
 
1.7%
85
 
0.7%
64
 
0.6%
Other values (2)5
 
0.7%

_embedded_show_officialSite
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct63
Distinct (%)54.8%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
nan
20 
https://www.hotstar.com/in/tv/criminal-justice-behind-closed-doors/1260049386
http://www.iqiyi.com/a_19rrhvpyyp.html
 
6
https://www.addatimes.com/show/feluda-pherot-web-series
 
6
https://play.hbomax.com/series/urn:hbo:series:GXkyDLAgeBY7CZgEAACHO
 
5
Other values (58)
70 

Length

Max length250
Median length68
Mean length45.84347826
Min length3

Characters and Unicode

Total characters5272
Distinct characters75
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique47 ?
Unique (%)40.9%

Sample

1st rowhttps://premier.one/show/8405
2nd rowhttps://start.ru/watch/257-prichin-chtoby-zhit
3rd rowhttps://www.kinopoisk.ru/series/1379016/
4th rowhttps://www.kinopoisk.ru/series/1379016/
5th rowhttps://more.tv/psih

Common Values

ValueCountFrequency (%)
nan20
 
17.4%
https://www.hotstar.com/in/tv/criminal-justice-behind-closed-doors/12600493868
 
7.0%
http://www.iqiyi.com/a_19rrhvpyyp.html6
 
5.2%
https://www.addatimes.com/show/feluda-pherot-web-series6
 
5.2%
https://play.hbomax.com/series/urn:hbo:series:GXkyDLAgeBY7CZgEAACHO5
 
4.3%
https://www.amazon.com/Off-the-Cuf/dp/B07R6PKR453
 
2.6%
https://premier.one/show/123392
 
1.7%
https://start.ru/watch/passazhiry2
 
1.7%
https://www.wavve.com/player/vod?programid=C9901_C99000000047&page=12
 
1.7%
https://www.beinconnect.com.tr/diziler/aile-sirketi2
 
1.7%
Other values (53)59
51.3%

Length

2022-05-09T21:20:35.918981image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nan20
 
17.4%
https://www.hotstar.com/in/tv/criminal-justice-behind-closed-doors/12600493868
 
7.0%
http://www.iqiyi.com/a_19rrhvpyyp.html6
 
5.2%
https://www.addatimes.com/show/feluda-pherot-web-series6
 
5.2%
https://play.hbomax.com/series/urn:hbo:series:gxkydlageby7czgeaacho5
 
4.3%
https://www.amazon.com/off-the-cuf/dp/b07r6pkr453
 
2.6%
https://www.kinopoisk.ru/series/13790162
 
1.7%
https://www.iqiyi.com/lib/m_213579814.html2
 
1.7%
https://www.svtplay.se/video/31148826/klassen2
 
1.7%
https://so.youku.com/search_video/q_%e9%a2%84%e6%94%af%e6%9c%aa%e6%9d%a5?spm=a2hbt.13141534.left-title-content-wrap.5~a2
 
1.7%
Other values (53)59
51.3%

Most occurring characters

ValueCountFrequency (%)
/401
 
7.6%
t364
 
6.9%
s281
 
5.3%
w246
 
4.7%
e235
 
4.5%
o223
 
4.2%
h208
 
3.9%
i203
 
3.9%
.202
 
3.8%
p180
 
3.4%
Other values (65)2729
51.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3413
64.7%
Other Punctuation792
 
15.0%
Decimal Number539
 
10.2%
Uppercase Letter388
 
7.4%
Dash Punctuation96
 
1.8%
Connector Punctuation23
 
0.4%
Math Symbol21
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t364
 
10.7%
s281
 
8.2%
w246
 
7.2%
e235
 
6.9%
o223
 
6.5%
h208
 
6.1%
i203
 
5.9%
p180
 
5.3%
a179
 
5.2%
r149
 
4.4%
Other values (16)1145
33.5%
Uppercase Letter
ValueCountFrequency (%)
A38
 
9.8%
C35
 
9.0%
P25
 
6.4%
E23
 
5.9%
B21
 
5.4%
L20
 
5.2%
O19
 
4.9%
D18
 
4.6%
Z16
 
4.1%
R15
 
3.9%
Other values (16)158
40.7%
Decimal Number
ValueCountFrequency (%)
182
15.2%
065
12.1%
660
11.1%
959
10.9%
456
10.4%
349
9.1%
845
8.3%
543
8.0%
243
8.0%
737
6.9%
Other Punctuation
ValueCountFrequency (%)
/401
50.6%
.202
25.5%
:131
 
16.5%
%39
 
4.9%
?14
 
1.8%
&3
 
0.4%
#1
 
0.1%
!1
 
0.1%
Math Symbol
ValueCountFrequency (%)
=17
81.0%
~2
 
9.5%
+2
 
9.5%
Dash Punctuation
ValueCountFrequency (%)
-96
100.0%
Connector Punctuation
ValueCountFrequency (%)
_23
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3801
72.1%
Common1471
 
27.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
t364
 
9.6%
s281
 
7.4%
w246
 
6.5%
e235
 
6.2%
o223
 
5.9%
h208
 
5.5%
i203
 
5.3%
p180
 
4.7%
a179
 
4.7%
r149
 
3.9%
Other values (42)1533
40.3%
Common
ValueCountFrequency (%)
/401
27.3%
.202
13.7%
:131
 
8.9%
-96
 
6.5%
182
 
5.6%
065
 
4.4%
660
 
4.1%
959
 
4.0%
456
 
3.8%
349
 
3.3%
Other values (13)270
18.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII5272
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/401
 
7.6%
t364
 
6.9%
s281
 
5.3%
w246
 
4.7%
e235
 
4.5%
o223
 
4.2%
h208
 
3.9%
i203
 
3.9%
.202
 
3.8%
p180
 
3.4%
Other values (65)2729
51.8%

_embedded_show_weight
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct47
Distinct (%)40.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.29565217
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2022-05-09T21:20:36.031075image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q115
median33
Q366.5
95-th percentile84
Maximum100
Range99
Interquartile range (IQR)51.5

Descriptive statistics

Standard deviation27.59639371
Coefficient of variation (CV)0.7206142772
Kurtosis-1.063309843
Mean38.29565217
Median Absolute Deviation (MAD)19
Skewness0.501183306
Sum4404
Variance761.5609458
MonotonicityNot monotonic
2022-05-09T21:20:36.141057image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
1412
 
10.4%
798
 
7.0%
37
 
6.1%
177
 
6.1%
686
 
5.2%
345
 
4.3%
185
 
4.3%
424
 
3.5%
383
 
2.6%
293
 
2.6%
Other values (37)55
47.8%
ValueCountFrequency (%)
12
 
1.7%
21
 
0.9%
37
6.1%
62
 
1.7%
71
 
0.9%
82
 
1.7%
101
 
0.9%
1412
10.4%
153
 
2.6%
177
6.1%
ValueCountFrequency (%)
1001
 
0.9%
941
 
0.9%
931
 
0.9%
901
 
0.9%
871
 
0.9%
843
 
2.6%
798
7.0%
771
 
0.9%
751
 
0.9%
731
 
0.9%

_embedded_show_dvdCountry
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
nan
114 
{'name': 'Korea, Republic of', 'code': 'KR', 'timezone': 'Asia/Seoul'}
 
1

Length

Max length70
Median length3
Mean length3.582608696
Min length3

Characters and Unicode

Total characters412
Distinct characters28
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.9%

Sample

1st rownan
2nd rownan
3rd rownan
4th rownan
5th rownan

Common Values

ValueCountFrequency (%)
nan114
99.1%
{'name': 'Korea, Republic of', 'code': 'KR', 'timezone': 'Asia/Seoul'}1
 
0.9%

Length

2022-05-09T21:20:36.250680image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T21:20:36.336552image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
nan114
93.4%
name1
 
0.8%
korea1
 
0.8%
republic1
 
0.8%
of1
 
0.8%
code1
 
0.8%
kr1
 
0.8%
timezone1
 
0.8%
asia/seoul1
 
0.8%

Most occurring characters

ValueCountFrequency (%)
n230
55.8%
a117
28.4%
'12
 
2.9%
e7
 
1.7%
7
 
1.7%
o5
 
1.2%
i3
 
0.7%
:3
 
0.7%
,3
 
0.7%
R2
 
0.5%
Other values (18)23
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter378
91.7%
Other Punctuation19
 
4.6%
Space Separator7
 
1.7%
Uppercase Letter6
 
1.5%
Open Punctuation1
 
0.2%
Close Punctuation1
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n230
60.8%
a117
31.0%
e7
 
1.9%
o5
 
1.3%
i3
 
0.8%
c2
 
0.5%
l2
 
0.5%
u2
 
0.5%
m2
 
0.5%
p1
 
0.3%
Other values (7)7
 
1.9%
Other Punctuation
ValueCountFrequency (%)
'12
63.2%
:3
 
15.8%
,3
 
15.8%
/1
 
5.3%
Uppercase Letter
ValueCountFrequency (%)
R2
33.3%
K2
33.3%
A1
16.7%
S1
16.7%
Space Separator
ValueCountFrequency (%)
7
100.0%
Open Punctuation
ValueCountFrequency (%)
{1
100.0%
Close Punctuation
ValueCountFrequency (%)
}1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin384
93.2%
Common28
 
6.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
n230
59.9%
a117
30.5%
e7
 
1.8%
o5
 
1.3%
i3
 
0.8%
R2
 
0.5%
c2
 
0.5%
l2
 
0.5%
u2
 
0.5%
K2
 
0.5%
Other values (11)12
 
3.1%
Common
ValueCountFrequency (%)
'12
42.9%
7
25.0%
:3
 
10.7%
,3
 
10.7%
{1
 
3.6%
/1
 
3.6%
}1
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n230
55.8%
a117
28.4%
'12
 
2.9%
e7
 
1.7%
7
 
1.7%
o5
 
1.2%
i3
 
0.7%
:3
 
0.7%
,3
 
0.7%
R2
 
0.5%
Other values (18)23
 
5.6%

_embedded_show_summary
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct65
Distinct (%)56.5%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
nan
10 
<p>Anuradha Chandra stabs her perfect lawyer husband one fateful night and confesses to her crime. However, it is anything but an open-and-shut case.</p>
<p><b>Feluda Pherot</b> is the return of the iconic Feluda, Asia's Brightest Crime Detector, along with his comrades Jatayu and Topshe, unraveling more mysteries.</p>
 
6
<p>Lin Luo Jing accidentally gets drawn into a game world where she is the daughter of the prime minister and meets all kind of beautiful men with different personalities. Among them are a sword deity, an imperial bodyguard, a playful rich man and an arrogant prince. The system informs her that she can only return to the real world after she finds her true love. While there seems tobe an abundance of good men around Luo Jing, there is one man she can't stand at all: the prince of the barbarian Yuan Kingdom Zhong Wu Mei. But out of all men, she ends up in an arranged marriage with Wu Mei.</p><p>Thus begins their love-hate relationship and her journey to find true love in order to win the game.</p>
 
6
<p>Lin Luojing enters the XR system due to a technology competition, and time-travels to the Sheng Yuan Dynasty of the game. To return back to reality, she has to find her true love and max the "favorability points". In the midst of exchanging tactics with arrogant prince Zhong Wu Mei, her former personal guard Liu Xiu Wen returns to the capital, this time with a new identity as the Persian Prince. Liu Xiu Wen vows to wage war on Zhong Wuyan. Facing both internal and external crises and conflicts, how will Lin Luojing resolve it and embark on her journey back home?</p>
 
6
Other values (60)
79 

Length

Max length1261
Median length556
Mean length359.5391304
Min length3

Characters and Unicode

Total characters41347
Distinct characters91
Distinct categories11 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique45 ?
Unique (%)39.1%

Sample

1st row<p>Marina is in her late 30s, she has a successful business and a close-knit family. Her husband is a surgeon and her daughters study at fancy establishments. To everybody her life seems perfect. Though, it is all just a facade concealing the real problems: her husband has a mistress, her elder daughter is a slacker and drug-dealer, her youngest is a sociopath. Well, Marina herself is not really a flower-lady, but a brothel-keeper who is hiding her dark business from everyone. The truth may come out when a girl of Marina's is found dead.</p>
2nd row<p>When terminal cancer patient Zhenya unexpectedly receives a clean bill of health, she can't believe it. She's in remission. But then her life implodes. Homeless, unemployed, and newly single - she stumbles across a list she wrote while she was sick of things she wanted to do when she got better. 257 of them - and now she won't give up until she checks off them all!</p>
3rd row<p>Stories about friendship and adventures of charming round heroes. Fun and musical, unexpected and dreamy, homely and adventurous. The whole world in one cozy chamomile valley.</p>
4th row<p>Stories about friendship and adventures of charming round heroes. Fun and musical, unexpected and dreamy, homely and adventurous. The whole world in one cozy chamomile valley.</p>
5th row<p>Oleg is a metropolitan psychotherapist. Clients of the central district of Moscow line up to him. Only lately Oleg doesn't like them, he tolerates them. Midlife crisis, life with mom at 40, loss of self-esteem, drug addiction, irritability and growing aggression. None of the clients are aware of his problems. From the outside, he seems successful, happily married, wealthy. Nobody knows the truth.</p><p> </p><p>A year ago, his wife went missing. She has been gone for 384 days.</p>

Common Values

ValueCountFrequency (%)
nan10
 
8.7%
<p>Anuradha Chandra stabs her perfect lawyer husband one fateful night and confesses to her crime. However, it is anything but an open-and-shut case.</p>8
 
7.0%
<p><b>Feluda Pherot</b> is the return of the iconic Feluda, Asia's Brightest Crime Detector, along with his comrades Jatayu and Topshe, unraveling more mysteries.</p>6
 
5.2%
<p>Lin Luo Jing accidentally gets drawn into a game world where she is the daughter of the prime minister and meets all kind of beautiful men with different personalities. Among them are a sword deity, an imperial bodyguard, a playful rich man and an arrogant prince. The system informs her that she can only return to the real world after she finds her true love. While there seems tobe an abundance of good men around Luo Jing, there is one man she can't stand at all: the prince of the barbarian Yuan Kingdom Zhong Wu Mei. But out of all men, she ends up in an arranged marriage with Wu Mei.</p><p>Thus begins their love-hate relationship and her journey to find true love in order to win the game.</p>6
 
5.2%
<p>Lin Luojing enters the XR system due to a technology competition, and time-travels to the Sheng Yuan Dynasty of the game. To return back to reality, she has to find her true love and max the "favorability points". In the midst of exchanging tactics with arrogant prince Zhong Wu Mei, her former personal guard Liu Xiu Wen returns to the capital, this time with a new identity as the Persian Prince. Liu Xiu Wen vows to wage war on Zhong Wuyan. Facing both internal and external crises and conflicts, how will Lin Luojing resolve it and embark on her journey back home?</p>6
 
5.2%
<p>Set in a world of anthropomorphic animals, Summer Camp Island follows two best friends Oscar, and Hedgehog, and Oscar who are dropped off at a surreal summer camp. The camp is a host to many odd occurrences such as: camp counselors who are composed of popular girls who know magic, horses that transform into unicorns, talking sharks, post-it notes that lead to other dimensions and nosy monsters that live under the bed. Oscar and Hedgehog must contend with these out of place events and make their stay at camp worthwhile.</p>5
 
4.3%
<p><b>Off the Cuff </b>explores the world's most unique, exciting, and often misrepresented communities.</p>3
 
2.6%
<p>Yoon Bo Mi of Apink Comedian Kim Min Kyoung, the former rhythmic gymnast Shin Soo Ji, Cheerleader Park Ki Ryang, Anchorwoman Park Ji Young, and Actress Kang So Yeon are huge fans of baseball. They come together to actually try playing baseball themselves instead of just watching it. Although they all work in different industries, they unite thanks to their mutual love of the sport. Together, they strive to compete against other amateur teams. Will their earnest desire to work together and their ambition to win make all the difference? How will they grow together as a team?</p>2
 
1.7%
<p>As a flower girl, Su Xiao Wan never really imagined her simple life would ever be anything other than ordinary. But when an accidental mixup lands her in a sibling contract with Gu Yan Xi, the young master of the well-known Gu family, her simple life is flipped upside down. Now obligated to play the part of Gu Yan Xi's younger sister, Xiao Wan finds herself tangled in the affairs of one of the most prominent business families in the land. Constantly hounded by Yang Xi's sharp tongue and ceaseless schemes, Xiao Wan is forced to rely on her quick wit and sly tricks, in order to survive. Never allowed a moment's peace, Xiao Wan can only find respite while in the company of Gu Zi Qian, the family's second master. A warm and caring young man, Zi Qian takes it upon himself to look after Xiao Wan, shielding her, whenever possible, from the constant pestering of his older brother. Unfortunately for Xiao Wan, Zi Qian's kindness is fueled by his own secret ambitions. Soon trapped in a complicated relationship with both Yang Xi and Zi Qian, Xiao Wan struggles to find a way out. As her head tells her to go one way, her heart tells her to go another; leaving Xiao Wan to wonder, which brother will ultimately help her find the happiness she desires?</p>2
 
1.7%
<p>‎At the end of the 20th century, due to the sudden decision of Xin Shensheng, gao Shan's business went bankrupt. Gao Shan wants to prove his father's innocence, but on his way suddenly falls in love with the daughter of Xin Shensheng, Tsin Waugh. Learning about the intentions of Gao Shan, Xin Shansheng makes him quit his job. ‎<br /><br />‎Gao Shan decides to go to Hong Kong to start from scratch, where he meets a benefactor and earns his first million in his life. Under the guidance of a mentor, he goes to Beijing and becomes a well-known investor. Soon Gao Shan meets Tsin Vo, who became a financial headhunter. Can love help them find their way to each other again? ‎<br /><br />‎Based on the novel by Xiao Moli "Little Storm 1.0"‎</p>2
 
1.7%
Other values (55)65
56.5%

Length

2022-05-09T21:20:36.484983image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the360
 
5.2%
and259
 
3.7%
a197
 
2.8%
of197
 
2.8%
to197
 
2.8%
in116
 
1.7%
her101
 
1.4%
is86
 
1.2%
with83
 
1.2%
she67
 
1.0%
Other values (1680)5317
76.2%

Most occurring characters

ValueCountFrequency (%)
6856
16.6%
e3827
 
9.3%
a2662
 
6.4%
t2551
 
6.2%
n2518
 
6.1%
o2317
 
5.6%
i2266
 
5.5%
s2058
 
5.0%
r2028
 
4.9%
h1751
 
4.2%
Other values (81)12513
30.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter31283
75.7%
Space Separator6866
 
16.6%
Uppercase Letter1310
 
3.2%
Other Punctuation1111
 
2.7%
Math Symbol624
 
1.5%
Dash Punctuation77
 
0.2%
Decimal Number47
 
0.1%
Format16
 
< 0.1%
Open Punctuation6
 
< 0.1%
Close Punctuation6
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e3827
12.2%
a2662
 
8.5%
t2551
 
8.2%
n2518
 
8.0%
o2317
 
7.4%
i2266
 
7.2%
s2058
 
6.6%
r2028
 
6.5%
h1751
 
5.6%
l1230
 
3.9%
Other values (23)8075
25.8%
Uppercase Letter
ValueCountFrequency (%)
T133
 
10.2%
S114
 
8.7%
W93
 
7.1%
L82
 
6.3%
A80
 
6.1%
X75
 
5.7%
M68
 
5.2%
C60
 
4.6%
B56
 
4.3%
H53
 
4.0%
Other values (16)496
37.9%
Other Punctuation
ValueCountFrequency (%)
,441
39.7%
.330
29.7%
/162
 
14.6%
'79
 
7.1%
"38
 
3.4%
:21
 
1.9%
?17
 
1.5%
!13
 
1.2%
;7
 
0.6%
%1
 
0.1%
Other values (2)2
 
0.2%
Decimal Number
ValueCountFrequency (%)
014
29.8%
26
12.8%
16
12.8%
55
 
10.6%
44
 
8.5%
63
 
6.4%
33
 
6.4%
82
 
4.3%
92
 
4.3%
72
 
4.3%
Space Separator
ValueCountFrequency (%)
6856
99.9%
 10
 
0.1%
Math Symbol
ValueCountFrequency (%)
>312
50.0%
<312
50.0%
Dash Punctuation
ValueCountFrequency (%)
-68
88.3%
9
 
11.7%
Format
ValueCountFrequency (%)
16
100.0%
Open Punctuation
ValueCountFrequency (%)
(6
100.0%
Close Punctuation
ValueCountFrequency (%)
)6
100.0%
Currency Symbol
ValueCountFrequency (%)
$1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin32593
78.8%
Common8754
 
21.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e3827
11.7%
a2662
 
8.2%
t2551
 
7.8%
n2518
 
7.7%
o2317
 
7.1%
i2266
 
7.0%
s2058
 
6.3%
r2028
 
6.2%
h1751
 
5.4%
l1230
 
3.8%
Other values (49)9385
28.8%
Common
ValueCountFrequency (%)
6856
78.3%
,441
 
5.0%
.330
 
3.8%
>312
 
3.6%
<312
 
3.6%
/162
 
1.9%
'79
 
0.9%
-68
 
0.8%
"38
 
0.4%
:21
 
0.2%
Other values (22)135
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII41304
99.9%
Punctuation25
 
0.1%
None18
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6856
16.6%
e3827
 
9.3%
a2662
 
6.4%
t2551
 
6.2%
n2518
 
6.1%
o2317
 
5.6%
i2266
 
5.5%
s2058
 
5.0%
r2028
 
4.9%
h1751
 
4.2%
Other values (71)12470
30.2%
Punctuation
ValueCountFrequency (%)
16
64.0%
9
36.0%
None
ValueCountFrequency (%)
 10
55.6%
č2
 
11.1%
ê1
 
5.6%
ã1
 
5.6%
ė1
 
5.6%
ö1
 
5.6%
ū1
 
5.6%
å1
 
5.6%

_embedded_show_updated
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct72
Distinct (%)62.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1632329541
Minimum1607964656
Maximum1652016543
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2022-05-09T21:20:36.638306image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1607964656
5-th percentile1608845017
Q11610509070
median1640018366
Q31648217029
95-th percentile1651947649
Maximum1652016543
Range44051887
Interquartile range (IQR)37707958.5

Descriptive statistics

Standard deviation17347919.91
Coefficient of variation (CV)0.01062770689
Kurtosis-1.640957974
Mean1632329541
Median Absolute Deviation (MAD)11820281
Skewness-0.3445991628
Sum1.877178972 × 1011
Variance3.009503251 × 1014
MonotonicityNot monotonic
2022-05-09T21:20:36.752842image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16088450178
 
7.0%
16088492216
 
5.2%
16518632666
 
5.2%
16096516766
 
5.2%
16393002025
 
4.3%
16410489583
 
2.6%
16404355312
 
1.7%
16407890402
 
1.7%
16469046062
 
1.7%
16400183662
 
1.7%
Other values (62)73
63.5%
ValueCountFrequency (%)
16079646561
 
0.9%
16088450178
7.0%
16088492216
5.2%
16096167881
 
0.9%
16096488852
 
1.7%
16096516766
5.2%
16097847492
 
1.7%
16097998962
 
1.7%
16101108411
 
0.9%
16109073001
 
0.9%
ValueCountFrequency (%)
16520165431
 
0.9%
16520047591
 
0.9%
16520040501
 
0.9%
16519907552
 
1.7%
16519813431
 
0.9%
16519332091
 
0.9%
16518632666
5.2%
16518386471
 
0.9%
16517638491
 
0.9%
16517491651
 
0.9%

_links_self_href
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct115
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
https://api.tvmaze.com/episodes/1977902
 
1
https://api.tvmaze.com/episodes/2005420
 
1
https://api.tvmaze.com/episodes/2050241
 
1
https://api.tvmaze.com/episodes/1950703
 
1
https://api.tvmaze.com/episodes/1996689
 
1
Other values (110)
110 

Length

Max length39
Median length39
Mean length39
Min length39

Characters and Unicode

Total characters4485
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique115 ?
Unique (%)100.0%

Sample

1st rowhttps://api.tvmaze.com/episodes/1977902
2nd rowhttps://api.tvmaze.com/episodes/2015818
3rd rowhttps://api.tvmaze.com/episodes/1964000
4th rowhttps://api.tvmaze.com/episodes/1995405
5th rowhttps://api.tvmaze.com/episodes/2007760

Common Values

ValueCountFrequency (%)
https://api.tvmaze.com/episodes/19779021
 
0.9%
https://api.tvmaze.com/episodes/20054201
 
0.9%
https://api.tvmaze.com/episodes/20502411
 
0.9%
https://api.tvmaze.com/episodes/19507031
 
0.9%
https://api.tvmaze.com/episodes/19966891
 
0.9%
https://api.tvmaze.com/episodes/20420031
 
0.9%
https://api.tvmaze.com/episodes/19963991
 
0.9%
https://api.tvmaze.com/episodes/19553181
 
0.9%
https://api.tvmaze.com/episodes/19967861
 
0.9%
https://api.tvmaze.com/episodes/19493361
 
0.9%
Other values (105)105
91.3%

Length

2022-05-09T21:20:36.868510image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://api.tvmaze.com/episodes/19779021
 
0.9%
https://api.tvmaze.com/episodes/20906551
 
0.9%
https://api.tvmaze.com/episodes/19640001
 
0.9%
https://api.tvmaze.com/episodes/19954051
 
0.9%
https://api.tvmaze.com/episodes/20077601
 
0.9%
https://api.tvmaze.com/episodes/19857891
 
0.9%
https://api.tvmaze.com/episodes/20396221
 
0.9%
https://api.tvmaze.com/episodes/20396231
 
0.9%
https://api.tvmaze.com/episodes/23244271
 
0.9%
https://api.tvmaze.com/episodes/23244281
 
0.9%
Other values (105)105
91.3%

Most occurring characters

ValueCountFrequency (%)
/460
 
10.3%
p345
 
7.7%
s345
 
7.7%
e345
 
7.7%
t345
 
7.7%
o230
 
5.1%
a230
 
5.1%
i230
 
5.1%
.230
 
5.1%
m230
 
5.1%
Other values (16)1495
33.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2875
64.1%
Other Punctuation805
 
17.9%
Decimal Number805
 
17.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
p345
12.0%
s345
12.0%
e345
12.0%
t345
12.0%
o230
8.0%
a230
8.0%
i230
8.0%
m230
8.0%
h115
 
4.0%
d115
 
4.0%
Other values (3)345
12.0%
Decimal Number
ValueCountFrequency (%)
9124
15.4%
2121
15.0%
0103
12.8%
1101
12.5%
370
8.7%
867
8.3%
665
8.1%
755
6.8%
453
6.6%
546
 
5.7%
Other Punctuation
ValueCountFrequency (%)
/460
57.1%
.230
28.6%
:115
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin2875
64.1%
Common1610
35.9%

Most frequent character per script

Common
ValueCountFrequency (%)
/460
28.6%
.230
14.3%
9124
 
7.7%
2121
 
7.5%
:115
 
7.1%
0103
 
6.4%
1101
 
6.3%
370
 
4.3%
867
 
4.2%
665
 
4.0%
Other values (3)154
 
9.6%
Latin
ValueCountFrequency (%)
p345
12.0%
s345
12.0%
e345
12.0%
t345
12.0%
o230
8.0%
a230
8.0%
i230
8.0%
m230
8.0%
h115
 
4.0%
d115
 
4.0%
Other values (3)345
12.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII4485
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/460
 
10.3%
p345
 
7.7%
s345
 
7.7%
e345
 
7.7%
t345
 
7.7%
o230
 
5.1%
a230
 
5.1%
i230
 
5.1%
.230
 
5.1%
m230
 
5.1%
Other values (16)1495
33.3%

Interactions

2022-05-09T21:20:28.466635image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:20:03.662331image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-05-09T21:20:12.411116image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-05-09T21:20:17.008933image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-05-09T21:20:11.258806image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:20:13.724088image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-05-09T21:20:14.326050image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-05-09T21:20:20.357790image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:20:23.375001image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:20:25.742002image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:20:28.061179image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:20:30.584896image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:20:08.340467image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2022-05-09T21:20:16.909469image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:20:21.409155image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:20:23.710607image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:20:26.092936image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:20:28.364743image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-05-09T21:20:36.948365image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-09T21:20:37.174562image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-09T21:20:37.316247image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-09T21:20:37.464414image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-05-09T21:20:37.711309image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-05-09T21:20:31.000702image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-05-09T21:20:31.673609image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-05-09T21:20:31.919422image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-05-09T21:20:32.034343image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

idurlnameseasonnumbertypeairdateairtimeairstampruntimesummary_embedded_show_id_embedded_show_url_embedded_show_name_embedded_show_type_embedded_show_language_embedded_show_genres_embedded_show_status_embedded_show_runtime_embedded_show_averageRuntime_embedded_show_premiered_embedded_show_ended_embedded_show_officialSite_embedded_show_weight_embedded_show_dvdCountry_embedded_show_summary_embedded_show_updated_links_self_href
01977900https://www.tvmaze.com/episodes/1977900/obycnaa-zensina-2x04-seria-13Серия 132.04.0regular2020-12-2410:002020-12-23T22:00:00+00:0054.0nan39115https://www.tvmaze.com/shows/39115/obycnaa-zensinaОбычная женщинаScriptedRussian['Drama', 'Crime', 'Mystery']Ended50.048.02018-10-292021-01-07https://premier.one/show/840536.0nan<p>Marina is in her late 30s, she has a successful business and a close-knit family. Her husband is a surgeon and her daughters study at fancy establishments. To everybody her life seems perfect. Though, it is all just a facade concealing the real problems: her husband has a mistress, her elder daughter is a slacker and drug-dealer, her youngest is a sociopath. Well, Marina herself is not really a flower-lady, but a brothel-keeper who is hiding her dark business from everyone. The truth may come out when a girl of Marina's is found dead.</p>1.610111e+09https://api.tvmaze.com/episodes/1977902
11963999https://www.tvmaze.com/episodes/1963999/257-pricin-ctoby-zit-2x09-seria-22Серия 222.09.0regular2020-12-24nan2020-12-24T00:00:00+00:0025.0nan43722https://www.tvmaze.com/shows/43722/257-pricin-ctoby-zit257 причин, чтобы житьScriptedRussian['Drama', 'Comedy']Ended25.024.02020-03-262021-01-21https://start.ru/watch/257-prichin-chtoby-zhit38.0nan<p>When terminal cancer patient Zhenya unexpectedly receives a clean bill of health, she can't believe it. She's in remission. But then her life implodes. Homeless, unemployed, and newly single - she stumbles across a list she wrote while she was sick of things she wanted to do when she got better. 257 of them - and now she won't give up until she checks off them all!</p>1.617284e+09https://api.tvmaze.com/episodes/2015818
21949912https://www.tvmaze.com/episodes/1949912/smesariki-novyj-sezon-1x33-zagvozdkaЗагвоздка1.033.0regular2020-12-24nan2020-12-24T00:00:00+00:006.0nan48151https://www.tvmaze.com/shows/48151/smesariki-novyj-sezonСмешарики. Новый сезонAnimationRussian['Comedy', 'Family']Running7.07.02020-05-18nanhttps://www.kinopoisk.ru/series/1379016/79.0nan<p>Stories about friendship and adventures of charming round heroes. Fun and musical, unexpected and dreamy, homely and adventurous. The whole world in one cozy chamomile valley.</p>1.646905e+09https://api.tvmaze.com/episodes/1964000
31949913https://www.tvmaze.com/episodes/1949913/smesariki-novyj-sezon-1x34-starinnyj-novogodnij-obycajСтаринный новогодний обычай1.034.0regular2020-12-24nan2020-12-24T00:00:00+00:006.0nan48151https://www.tvmaze.com/shows/48151/smesariki-novyj-sezonСмешарики. Новый сезонAnimationRussian['Comedy', 'Family']Running7.07.02020-05-18nanhttps://www.kinopoisk.ru/series/1379016/79.0nan<p>Stories about friendship and adventures of charming round heroes. Fun and musical, unexpected and dreamy, homely and adventurous. The whole world in one cozy chamomile valley.</p>1.646905e+09https://api.tvmaze.com/episodes/1995405
41960733https://www.tvmaze.com/episodes/1960733/psih-1x08-vozrozdenieВозрождение1.08.0regular2020-12-2412:002020-12-24T00:00:00+00:0070.0nan49280https://www.tvmaze.com/shows/49280/psihПсихScriptedRussian['Drama', 'Thriller']Ended62.062.02020-11-052020-12-24https://more.tv/psih29.0nan<p>Oleg is a metropolitan psychotherapist. Clients of the central district of Moscow line up to him. Only lately Oleg doesn't like them, he tolerates them. Midlife crisis, life with mom at 40, loss of self-esteem, drug addiction, irritability and growing aggression. None of the clients are aware of his problems. From the outside, he seems successful, happily married, wealthy. Nobody knows the truth.</p><p> </p><p>A year ago, his wife went missing. She has been gone for 384 days.</p>1.619195e+09https://api.tvmaze.com/episodes/2007760
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71987502https://www.tvmaze.com/episodes/1987502/passaziry-1x01-svetlana-i-igorСветлана и Игорь1.01.0regular2020-12-24nan2020-12-24T00:00:00+00:0021.0nan52499https://www.tvmaze.com/shows/52499/passaziryПассажирыScriptedRussian['Drama', 'Supernatural']Running22.023.02020-12-24nanhttps://start.ru/watch/passazhiry84.0nannan1.651991e+09https://api.tvmaze.com/episodes/2039623
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Last rows

idurlnameseasonnumbertypeairdateairtimeairstampruntimesummary_embedded_show_id_embedded_show_url_embedded_show_name_embedded_show_type_embedded_show_language_embedded_show_genres_embedded_show_status_embedded_show_runtime_embedded_show_averageRuntime_embedded_show_premiered_embedded_show_ended_embedded_show_officialSite_embedded_show_weight_embedded_show_dvdCountry_embedded_show_summary_embedded_show_updated_links_self_href
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1141976648https://www.tvmaze.com/episodes/1976648/wwe-nxt-uk-2020-12-24-episode-52Episode 522020.052.0regular2020-12-2415:002020-12-24T20:00:00+00:0060.0nan39053https://www.tvmaze.com/shows/39053/wwe-nxt-ukWWE NXT UKSportsEnglish[]Running60.060.02018-10-17nannan84.0nan<p>The one-hour episodes will feature the biggest names from NXT UK, including Pete Dunne, Mark Andrews, Rhea Ripley, Toni Storm, Tyler Bate, Trent Seven and Wolfgang. Joining the NXT UK broadcasting team as backstage interviewer is British broadcasting personality Radzi Chinyanganya, best known for hosting ITV game show "Cannonball," and in his ongoing role as a presenter of the world's longest-running children's TV show, the BBC's "Blue Peter." Calling the action are commentators Nigel McGuinness and Vic Joseph, joined by ring announcer Andy Shepherd and NXT UK General Manager, the legendary Johnny Saint.</p>1.652005e+09https://api.tvmaze.com/episodes/2044707